bluuebunny/crossref_metadata_2025 huggingface dataset:
Development and Performance Evaluation of a Selective Plugging System for High-Temperature and High-Salinity Water-Bearing Gas Reservoir
Authors: Guo Panyang , Ma Yongle , Jiang Houshun , Wang Jie , Xu Hualei
Published: September 2022

In the development of natural gas reservoirs, the water produced in wells will cause a decline in gas well productivity. In this study, a selective frozen gel plugging agent system suitable for water plugging in gas reservoirs under conditions of high temperature and high salinity. The plugging capacity of the gel system was evaluated. The experimental results showed that: (1) the optimal experimental scheme of the frozen gelling system was as follows: 107 °C with 1.0% AM-AMPS +0.6% p-benzenediol +0.6% hexamethylenetetramine +0.2% thiourea. (2) The optimal injection volume of the system for water phase plugging was 0.5 PV, and the system has an excellent plugging effect at a permeability of 356–3118 mD. (3) The system could also effectively improve the degree of heterogeneity, and the smaller the degree of heterogeneity, the better the improvement effect. (4) The gel system had a good effect on different plugging methods, and the gas medium did not affect the plugging performance. This study provides a sufficient theoretical basis for the exploitation of high-temperature and high-salinity water-bearing gas reservoirs through experimental research, which is of great significance for improving production capacity.
Publisher’s Note: Psychoactives, a New Open Access Journal
Authors: Craciun Ioana
Published: September 2022

Basel is sometimes referred to as the birthplace of hallucinogenic science, where in 1938 Albert Hofmann first discovered lysergic acid diethylamide (LSD) [...]
The Effects of Behavioral Restrictions on the Spread of COVID-19
Authors: Sasaki Kenji , Ikeda Yoichi , Nakano Takashi
Published: September 2022

Several measures, including behavioral restrictions for individuals, have been taken throughout the world to control the spread of COVID-19. The aim of these measures is to prevent infected persons from coming into contact with susceptible persons. Since the behavioral restrictions for all citizens, such as city-wide lockdowns, were directly linked to the stagnation of economic activities, the assessment of such measures is crucial. In order to evaluate the effects of behavioral restrictions, we employed the broken-link model to compare the situation of COVID-19 in Shanghai, where a lockdown was implemented from March to June 2022, with the situation in Taiwan, where the spread of COVID-19 has been well controlled so far. The results show that the small link-connection probability was achieved by the substantial isolation of infected persons, including the lockdown measures. Although the strict measures for behavioral restrictions were effective to reduce the total number infected people, the daily reported cases per one million people followed the curve evaluated by the broken-link model. This result considers that infections are unavoidable for the population.
A Tool for the Selection of Food Waste Management Approaches for the Hospitality and Food Service Sector in the UK
Authors: Gerassimidou Spyridoula , Dora Manoj , Iacovidou Eleni
Published: September 2022

The UK government has been calling for action in tackling food waste (FW) generation, to which the Hospitality and Food Services (HaFS) sector contributes substantially. Decision-making tools that inform the selection of appropriate FW management (FWM) processes in the HaFS sector are lacking. This study fills this gap by offering a conceptual decision-making tool that supports selecting appropriate and commercially available FW processing techniques for the HaFS sector. The study initially conducted an exploratory analysis of on-site and off-site FWM options commercially available in the UK to inform the development of a two-tier decision-making framework. A set of steering criteria was developed and refined via stakeholder consultations to create flowcharts that guide the selection of FWM options, i.e., Tier 1 of the framework. Tier 2 refines the FWM process selection using a comparative sustainability scorecard of FWM options performance developed through a rapid systematic evidence mapping. The main outcome of this study is a flexible decision-making tool that allows stakeholders to participate in the decision-making process and facilitate the selection of tailored-based FWM processes that better suit their circumstances and needs. This approach to decision-making is more likely to enable solutions that facilitate the sustainable management of wasted resources and promote circularity.
Replacing Surf and Turf Medical Care: A Clarion Call for the Incorporation of Rheumatology as an Integral Component of Primary Care Education
Authors: Rothschild Bruce
Published: September 2022

The current time/experience allotted for rheumatology in primary care education seems like paying lip service to a medical education clinical approach consisting of: 1 [...]
Comprehensive Remote Sensing Technology for Monitoring Landslide Hazards and Disaster Chain in the Xishan Mining Area of Beijing
Authors: Jiao Runcheng , Wang Shengyu , Yang Honglei , Guo Xuefei , Han Jianfeng , Pei Xin , Yan Chi
Published: September 2022

The Xishan coal mine area in Beijing, China has a long history of mining. Many landslide hazards, in addition to collapses and ground fractures, have occurred in this area. This study used multi-temporal satellite images to extract this region’s deformation information, identify landslides and analyze the deformation evolution process of these landslides. Taking the Anzigou ditch as an example, we investigate the “Quarry–Landslide–Mudslide” disaster chain model. We found that the landslide evolution process is closely related to the geological conditions, and usually goes through four stages: initial deformation, slope front swelling and collapsing, rear part connecting and rupturing, and landslide creeping. The surface deformation can be identified and tracked by high-resolution optical images and InSAR monitoring. Under the combined effects of rainfall and topographic conditions, medium and large landslides may occur and trigger a “Quarry–Landslide–Mudflow” disaster chain. The identification and analysis of these landslide hazards and the disaster chain help with geological disaster prevention, and provide reference for early identification and research of similar disasters.
Building Function Type Identification Using Mobile Signaling Data Based on a Machine Learning Method
Authors: Nie Wenyu , Fan Xiwei , Nie Gaozhong , Li Huayue , Xia Chaoxu
Published: September 2022

Identifying building function type (BFT) is vital for many studies and applications, such as urban planning, disaster risk assessment and management, and traffic control. Traditional remote sensing methods are commonly used for land use/cover classification, but they have some limitations in BFT identification. Considering that the dynamic variations of social sensing mobile signaling (MS) data at diurnal and daily scales are directly related to BFT, in this paper, we propose a method to infer BFT using MS data obtained from mobile devices. First, based on the different patterns of population dynamics within different building types, we propose a BFT classification scheme with five categories: residential (R), working (W), entertainment (E), visiting (V), and hospital (H). Then, a random forest (RF) classification model is constructed based on two days (one workday and one weekend) of MS data with a temporal resolution of one hour to identify the BFT. According to the cross-validation method, the overall classification accuracy is 84.89%, and the Kappa coefficient is 0.78. Applying the MS data-constructed RF model to the central areas of Beijing Dongcheng and Xicheng Districts, the overall detection rate is 97.35%. In addition, to verify the feasibility of the MS data, the Sentinel-2 (S2) remote sensing data are used for comparison, with a classification accuracy of 73.33%. The better performance of the MS method shows its excellent potential for BFT identification, as the spatial and temporal population dynamics reviewed based on MS data are more correlated with BFT than geometric or spectral features in remote sensing images. This is an innovative attempt to identify BFT with MS data, and such a method compensates for the scarcity of BFT studies driven by population dynamics. Overall, in this study, we show the feasibility of using time series MS data to identify BFT and we provide a new path for building function mapping at large scales.
A Robust Star Identification Algorithm Based on a Masked Distance Map
Authors: Yuan Hao , Li Dongxu , Wang Jie
Published: September 2022

The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function.
Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality
Authors: Chifor Radu , Hotoleanu Mircea , Marita Tiberiu , Arsenescu Tudor , Socaciu Mihai Adrian , Badea Iulia Clara , Chifor Ioana
Published: September 2022

This research aimed to evaluate Mask R-CNN and U-Net convolutional neural network models for pixel-level classification in order to perform the automatic segmentation of bi-dimensional images of US dental arches, identifying anatomical elements required for periodontal diagnosis. A secondary aim was to evaluate the efficiency of a correction method of the ground truth masks segmented by an operator, for improving the quality of the datasets used for training the neural network models, by 3D ultrasound reconstructions of the examined periodontal tissue. Methods: Ultrasound periodontal investigations were performed for 52 teeth of 11 patients using a 3D ultrasound scanner prototype. The original ultrasound images were segmented by a low experienced operator using region growing-based segmentation algorithms. Three-dimensional ultrasound reconstructions were used for the quality check and correction of the segmentation. Mask R-CNN and U-NET were trained and used for prediction of periodontal tissue’s elements identification. Results: The average Intersection over Union ranged between 10% for the periodontal pocket and 75.6% for gingiva. Even though the original dataset contained 3417 images from 11 patients, and the corrected dataset only 2135 images from 5 patients, the prediction’s accuracy is significantly better for the models trained with the corrected dataset. Conclusions: The proposed quality check and correction method by evaluating in the 3D space the operator’s ground truth segmentation had a positive impact on the quality of the datasets demonstrated through higher IoU after retraining the models using the corrected dataset.
Electrochemical Biosensing of Glucose Based on the Enzymatic Reduction of Glucose
Authors: Soranzo Thomas , Ben Tahar Awatef , Chmayssem Ayman , Zelsmann Marc , Vadgama Pankaj , Lenormand Jean-Luc , Cinquin Phillipe , K. Martin Donald , Zebda Abdelkader
Published: September 2022

In this work, the enzyme aldehyde reductase, also known as aldose reductase, was synthesized and cloned from a human gene. Spectrophotometric measurements show that in presence of the nicotinamide adenine dinucleotide phosphate cofactor (NADPH), the aldehyde reductase catalyzed the reduction of glucose to sorbitol. Electrochemical measurements performed on an electrodeposited poly(methylene green)-modified gold electrode showed that in the presence of the enzyme aldehyde reductase, the electrocatalytic oxidation current of NADPH decreased drastically after the addition of glucose. These results demonstrate that aldehyde reductase is an enzyme that allows the construction of an efficient electrochemical glucose biosensor based on glucose reduction.
Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems
Authors: Sang Shangyu , Zhang Ruikun , Lin Xue
Published: September 2022

This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) to solve the bipartite containment problem of MASs. The designed controller only relies on the input and output data of the agent without requiring the model information of MASs. Secondly, we give the convergence condition that the containment error asymptotically converges to zero. The result shows that the output states of all followers will converge to the convex hull formed by the output states of leaders and the symmetric output states of leaders. Finally, the simulation verifies the effectiveness of the proposed method.
Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition
Authors: Nan Mihai , Florea Adina Magda
Published: September 2022

Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human–computer interaction can be achieved, and improved assistance can be provided by social robots in real-time scenarios. In this context, the performance of the prediction system is a key aspect. The purpose of this paper is to introduce a neural network approach based on various types of convolutional layers that can achieve a good performance in recognizing actions but with a high inference speed. The experimental results show that our solution, based on a combination of graph convolutional networks (GCN) and temporal convolutional networks (TCN), is a suitable approach that reaches the proposed goal. In addition to the neural network model, we design a pipeline that contains two stages for obtaining relevant geometric features, data augmentation and data preprocessing, also contributing to an increased performance.
A Method Used to Improve the Dynamic Range of Shack–Hartmann Wavefront Sensor in Presence of Large Aberration
Authors: Yang Wen , Wang Jianli , Wang Bin
Published: September 2022

With the successful application of the Shack–Hartmann wavefront sensor in measuring aberrations of the human eye, researchers found that, when the aberration is large, the local wavefront distortion is large, and it causes the spot corresponding to the sub-aperture of the microlens to shift out of the corresponding range of the sub-aperture. However, the traditional wavefront reconstruction algorithm searches for the spot within the corresponding range of the sub-aperture of the microlens and reconstructs the wavefront according to the calculated centroid, which leads to wavefront reconstruction errors. To solve the problem of the small dynamic range of the Shack–Hartmann wavefront sensor, this paper proposes a wavefront reconstruction algorithm based on the autocorrelation method and a neural network. The autocorrelation centroid extraction method was used to calculate the centroid in the entire spot map in order to obtain a centroid map and to reconstruct the wavefront by matching the centroid with the microlens array through the neural network. This method breaks the limitation of the sub-aperture of the microlens. The experimental results show that the algorithm improves the dynamic range of the first 15 terms of the Zernike aberration reconstruction to varying degrees, ranging from 62.86% to 183.87%.
An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning
Authors: Kim Donghyun , Son Kyuho , Han Dongsoo
Published: September 2022

Wi-Fi fingerprinting is the most popular indoor positioning method today, representing received signal strength (RSS) values as vector-type fingerprints. Passive fingerprinting, unlike the active fingerprinting method, has the advantage of being able to track location without user participation by utilizing the signals that are naturally emitted from the user’s smartphone. However, since signals are generated depending on the user’s network usage patterns, there is a problem in that data are irregularly collected according to the patterns. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performances for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, it is expected that the stability of indoor positioning and the quality of location-based services will improve.
Water Color Identification System for Monitoring Aquaculture Farms
Authors: Chen Hsiang-Chieh , Xu Sheng-Yao , Deng Kai-Han
Published: September 2022

This study presents a vision-based water color identification system designed for monitoring aquaculture ponds. The algorithm proposed in this system can identify water color, which is an important factor in aquaculture farming management. To address the effect of outdoor lighting conditions on the proposed system, a color correction method using a color checkerboard was introduced. Several candidates for water-only image patches were extracted by performing image segmentation and fuzzy inferencing. Finally, a deep learning-based model was employed to identify the color of these patches and then find the representative color of the water. Experiments at different aquaculture sites verified the effectiveness of the proposed system and its algorithm. The color identification accuracy exceeded 96% for the test data.
Millimeter-Wave Imaging System Based on Direct-Conversion Focal-Plane Array Receiver
Authors: Korolyov Sergey , Goryunov Aleksandr , Illarionov Ivan , Parshin Vladimir , Zemlyanukha Petr
Published: September 2022

A new approach to millimeter-wave imaging was suggested and experimentally studied. This approach can be considered as the evolution of the well-established focal-plane array (FPA) millimeter-wave imaging. The significant difference is the use of a direct-conversion array receiver, instead of the direct-detection array receiver, along with the frequency-modulated continuous-wave (FMCW) radar technique. The sensitivity of the direct-conversion receiver is several orders higher than the sensitivity of the direct-detection one, which allows us to increase the maximum imaging range by more than one order of magnitude. The additional advantage of the direct-conversion technique is the opportunity to obtain information about the range to an object. The realization of the direct-conversion FPA imaging system was made possible due to original sensitive simple-designed receiving elements based on low-barrier Mott diodes. The suggested imaging method’s main characteristics, which include the achievable angular and range resolution and the achievable maximum imaging range, were studied. A maximum range of up to 100 m was experimentally determined. A 94 GHz 8 × 8 imaging system was developed for demonstration purposes and studied in detail. The suggested technique is assumed to be useful for creating a long-range millimeter-wave camera, in particular, for robotic systems that operate in poor environmental conditions.
Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios
Authors: Kheiralipour Kamran , Nadimi Mohammad , Paliwal Jitendra
Published: September 2022

Rapid, non-destructive, and smart assessment of the maturity levels of fruit facilitates their harvesting and handling operations throughout the supply chain. Recent studies have introduced machine vision systems as a promising candidate for non-destructive evaluations of the ripeness levels of various agricultural and forest products. However, the reported models have been fruit-specific and cannot be applied to other fruit. In this regard, the current study aims to evaluate the feasibility of estimating the ripeness levels of wild pistachio fruit using image processing and artificial intelligence techniques. Images of wild pistachios at four ripeness levels were recorded using a digital camera, and 285 color and texture features were extracted from 160 samples. Using the quadratic sequential feature selection method, 16 efficient features were identified and used to estimate the maturity levels of samples. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and an artificial neural network (ANN) were employed to classify samples into four ripeness levels, including initial unripe, secondary unripe, ripe, and overripe. The developed machine vision system achieved a correct classification rate (CCR) of 93.75, 97.5, and 100%, respectively. The high accuracy of the developed models confirms the capability of the low-cost visible imaging system in assessing the ripeness of wild pistachios in a non-destructive, automated, and rapid manner.
Evaluation of Hydraulic-Tunnel-Lining Durability Based on ANP and Cloud-Model-Improved Matter–Element Theory
Authors: Li Qingfu , Fan Chuangshi
Published: September 2022

Compared with highway tunnels, hydraulic tunnel linings are in the water environment for a long time, and their lining materials and structures are more vulnerable to damage. Therefore, a comprehensive scientific durability evaluation of hydraulic tunnel linings is of great significance for the safe operation and daily maintenance of hydraulic tunnels. This paper proposes a new method for evaluating the durability of hydraulic tunnel linings. The paper first constructs a hydraulic tunnel concrete lining durability evaluation system, taking into account the feedback between the indices, and using the analytic network process (ANP) to calculate the weights of each index, as well as multiple-expert scoring to reduce its one-sidedness and subjectivity. Considering the randomness and fuzzy nature of the evaluation, the cloud model was used to modify the matter–element theory to evaluate the durability of the hydraulic tunnel lining. Finally, an example application was carried out, and the durability classes of five segments of the Nawei Tunnel were calculated as III, II, II, II, and III. The results were compared with the evaluation results of the method in the related literature, which proved that the method has good accuracy in evaluating the durability of hydraulic tunnel linings.
Toward Tourists–Media–Cities Tourism: Xi’an as a Wanghong City
Authors: Fan Tingting , Lin Zhongxuan
Published: September 2022

This study investigated the phenomenon of wanghong cities in China to illustrate the dynamic relationships between media, tourists, and cities in the new normal of the post-COVID-19 era. Specifically, this study proposes the innovative analytical framework of tourists–media–cities (“ToMeCi”), which is grounded in tourism studies, media studies, and urban studies, but goes beyond the traditional concepts and previous studies of media and city, tourism and media, and tourism and city. Based on a case study of Xi’an, one of the most famous wanghong cities in China, this study attempted to answer the following research questions: how can the deployment of creative media practices create new digital tourism patterns in the specific Chinese context of wanghong cities; moreover, why is this reasonable and possible, and what are the implications? For the purpose of this study, we employed a qualitative research method and conducted online and offline ethnographic fieldwork, textual analysis, and in-depth interviews with 22 tourists and 26 short-video producers or live streamers. The findings reveal that the city of Xi’an was transformed into a wanghong city to attract tourists, who interact with the city through specific media practices of clocking in and live streaming, with a new digital tourism model of the cyberflaneur emerging against the specific backdrop of the COVID-19 pandemic. Finally, we discuss the possible contributions and limitations of the phenomenon.
Reduction of High-Chromium-Containing Wastewater in the Leaching of Pyritic Waste Rocks from Coal Mines
Authors: Silva Rodrigo de Almeida , Secco Marina Paula , Menezes Jean Carlo Salomé dos Santos , Schneider Ivo André Homrich , Lermen Richard Thomas
Published: September 2022

Coal is an abundant resource which can be used to produce low-cost energy; however, its usage causes great environmental damage. Before mineral coal can be used, it must be processed to remove coal tailings. These tailings contain pyrite and accumulate in large dumps, presenting significant environmental liabilities, such as acid mine drainage. Another industry that generates environmental liabilities is the chrome-plating industry, mainly because it produces hexavalent chromium (Cr6+) waste. The main aim of this work was to evaluate Cr6+ as a reduction agent in trivalent chromium (Cr3+) conversion in the leaching of coal-mine waste containing pyrite. Cr3+ is about 100 times less toxic than Cr6+ and can be easily removed from industrial effluents by alkaline precipitation. There are several sources of effluents containing Cr6+—a compound which is known worldwide to be toxic, carcinogenic, and mutagenic. A leaching and treatment device was developed and tested for waste treatment. The results indicated that the developed treatment system reduced 100% of Cr6+ to Cr3+ through pyrite leaching in a Cr6+ wastewater sample from the electroplating industry. In addition, the chromium sludge resulting from the treatment process, after calcination, was tested in a ceramic glaze as a pigment and, when compared with an industrial pigment, showed similar mineralogical characteristics.
Alfalfa Cover Crops Influence the Soil Fungal Community and Function in Apple Orchards in Arid Desert Oases in Northwest China
Authors: Wang Zhenlei , Zhao Weiliang , Xi Linqiao
Published: September 2022

The present study investigated the effects of alfalfa cover crops on soil fungal communities and function in apple orchards in arid desert oases. A five-year apple orchard was subjected to two treatments: Intercropping with an alfalfa cover crop (A) and clean tillage (QG). The soil fungal ITS (internal transcribed spacer) region was analyzed using Illumina MiSeq high-throughput sequencing technology, and fungal function was determined using FUNGuild. Changes in the fungal community structure, diversity, and metabolic function in the 0–60 cm soil layer of the apple orchard were compared. The results showed that the alfalfa cover crops enhanced fungal richness but reduced diversity. The alfalfa cover crops improved fungal copy numbers but reduced the relative abundance of the dominant phylum, Ascomycota. Correlations between soil fungi and soil factors revealed that total nitrogen and total carbon were the most important nutrient factors in positively regulating the fungal community. The main negative factors were soil total salts and pH. The FUNGuild functional prediction showed that Ectomycorrhizal-Wood Saprotroph and Endophyte-Undefined Saprotroph only appeared in the alfalfa cover crops. The abundance of endophytes was enhanced (p < 0.05), but the abundance of plant pathogens and wood saprotrophs decreased (p < 0.01). Alfalfa cover crops could increase the copy numbers and richness in arid oasis apple orchards.
Sustainable Agricultural Development Assessment: A Comprehensive Review and Bibliometric Analysis
Authors: Yu Shu , Mu Yongtong
Published: September 2022

This study used a bibliometric analysis of 110 scientific papers published between 2002 and 2022 to overview the publication trends and growth potential of sustainable agricultural development assessment studies. The findings showed that the collaboration between authors and institutions was not strong, the journals published were relatively scattered, the coverage of disciplines was wide, more papers were published in higher impact journals, and the authors of the sample articles were mostly from Asian and European countries, the co-citation analysis pointed out more influential authors and journals. The temporal evolution of the keywords identified that researchers focused more on the sustainable operation of agriculture and the methods to assess the degree of sustainability initially, as research progressed and more scientific methods were applied, recent agricultural sustainability research focused on environmental impacts and economic efficiency. According to statistical analysis, the primary level is mostly carried out in three dimensions (economic, social, and environmental), with reference to this principle, this paper summarized the specific indicators appearing in the sample articles and divided them into three subgroups. The results showed that the indicators were selected from a wide range of sources, the entropy weight method and Analytic Hierarchy Process were the most frequent methods of assigning weights to indicators. The present study concludes that the sustainable agricultural development assessment studies are still immature, there is still much room for research on the application of sustainability assessment theory to agrarian systems.
Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk
Authors: Žigienė Gerda , Rybakovas Egidijus , Vaitkienė Rimgailė , Gaidelys Vaidas
Published: September 2022

As supply chains (SCs) become more complex globally, businesses are looking for efficient business analytics (BA), business intelligence (BI), and artificial intelligence (AI) tools for managing supply-chain risk. The tools and methodologies proposed by the supply-chain risk management (SCRM) literature are mostly based on experts’ judgments, their knowledge, and past data. The expert evaluation-based approach could be partly or fully replaced by AI solutions, increasing objectivity, impartiality, and impersonality, reducing sources of human mistakes, biases, and inefficiencies in SCRM. However, the transition from BA to AI in SCRM is not a self-contained process; though attractive as a vision, it is not straightforward as a management or implementation process. The purpose of this research is to explore and define the conceptual grounds for transitioning from BA to AI in SCRM. The conceptual SCRM structure, its AI suitability, and implementation terms are defined theoretically based on a literature review. A single, in-depth business case study is employed to explore the theoretically defined terms of AI-based SCRM implementation. The proposed conceptual AI-suitable SCRM structure is defined by five principal building blocks: risk events, risk-event indicators, data-processing rules and algorithms, analytical techniques, and risk event probability forecasts. The study concludes that the business environment meets AI-based SCRM-implementation terms of data existence and access. Since data on risk events and negative outcomes are limited for machine learning, experts’ experience and knowledge might be utilised to build initial rules and data-processing algorithms for AI.
An Adaptive Switching Control Strategy under Heavy–Light Load for the Bidirectional LLC Considering Parasitic Capacitance
Authors: Han Yetong , Wang Rui , Zhang Yi , Ma Dazhong , Jiang Shaoxv , Wen Liangwu
Published: September 2022

The LLC topology is widely used to link renewable energy and inverters to provide constant voltage in the smart grid. Due to its characteristics, the voltage regulation range under light load conditions is limited, so that the output voltage cannot be maintained constant. The adaptive switching control strategy is proposed in this paper to keep the output constant. Under heavy load conditions, the voltage is kept constant by adjusting the frequency to ensure the accuracy of the control. The phase shift is adjusted to achieve constant voltage, considering the influence of parasitic capacitance on the modeling process for the changing trend of output voltage in light load conditions. The switching point is calculated from the characteristic curve to ensure that the output voltage is stable during mode switching. In addition, there is a new hysteresis control which is robust near the switching point to cope with the instability of the new energy itself and frequent disturbance under light load. Finally, a 400V–36V–1KW prototype is used to verify this control strategy.
A Bibliometric Analysis of Third-Party Logistics Services Providers (3PLSP) Selection for Supply Chain Strategic Advantage
Authors: Qureshi Mohamed Rafik Noor Mohamed
Published: September 2022

Third-party logistics (3PL) service providers are crucial to achieving sustainability in supply chain management (SCM). Third-party logistics service providers (3PLSP) are identified in such a way that they can help a firm fulfill its targeted objectives. The choice of 3PLSP must take into account broad selection criteria that are pertinent to both current and future needs. Various modelling techniques have been applied to evaluate and select 3PLSP. For the evaluation and selection of 3PLSP in the given set of situational requirements, a set of different selection criteria has been set up. This paper screens 191 articles from the Scopus database and finds 132 articles spread over two decades from 2001 to 2022 (30 July 2022). The 132 filtered articles are further assessed using bibliometric analysis, qualitatively and quantitatively. The identified research articles are studied and analyzed according to different bibliometric parameters, using MS Excel, VOSviewer version 1.6.17, and Biblioshiny 4.0. It has been found that the top five countries engaged in research are India (36%), Turkey (13%), Taiwan (12%), China (11%), and the USA (10%). The top three journals with the most research publications were International Journal of Logistics Systems and Management, Benchmarking, and Expert Systems with Applications. The three research methodologies most often used to solve 3PL selection problems were fuzzy-based, AHP, and TOPSIS. Numerous studies of theoretical models have applications in real-world situations. The developed selection models have high applicability in the automobile industry, agricultural industries, marine products, etc.
Internal Communication in R&D: Decision-Making Methods Based on Expert Approaches
Authors: Zemlickienė Vaida , Lapinskaitė Indrė , Turskis Zenonas
Published: September 2022

Relatively recently, internal communication received little attention from theorists, since they focused on external communication. The importance of internal communication is seen as the taker of responsibility for all internal exchanges of information between internal stakeholders at all levels. However, interdisciplinary cooperation in terms of internal communication is a significant issue for any organization running the R&D process. Different studies have already revealed strong links between communication and R&D success, but a lack of research continuity is visible, especially one relevant in practice. This article examines communication in the R&D process through the prism of decision making. It aims to take experience from R&D experts to supplement a list of decision-making methods used in the R&D process, which is compiled based on literature analysis. In the next stage of the investigation, the list of decision-making methods is used to determine the weights of the methods, which reflect the suitability of the decision-making methods in the R&D process. In the final stage of the investigation, differences in the evaluations of the US and German experts are presented, reflecting different experiences and differences in the conditions of the business environment.
Full-Scale Experimental Study on Prefabricated Greening Ecological Retaining Walls
Authors: Wang Xinquan , Li Xiao , Zhu Cong , Diao Hongguo , Wang Kangyu , Huang Tianyuan , Tu Jiewen , Que Yichen
Published: September 2022

Prefabricated walls are frequently utilized as retaining structures in different applications. A new type of prefabricated greening ecological retaining wall (PGERW) is proposed in this research. Full-scale tests and numerical simulations were conducted to investigate the stress characteristics of the PGERW. To this end, the load–stress relationship, load–displacement relationship, and crack development of the retaining wall columns were carefully evaluated. It was found that when the load acting on the 3 m high column reached the ultimate load-bearing capacity (about 150 kN), an “arc + 7”-shaped crack pattern emerged. A V-shaped crack composed of bolt–chamfer cracks formed when the load applied to a 2.5 m high column reached the ultimate load-bearing capacity (about 335 kN). The design of hollow thin-walled columns can effectively reduce the amount of concrete used and, as a consequence, reduce its carbon emissions, while meeting the design strength requirements of the retaining wall. The PGERW addresses the challenges of improving the extent of greening and drainage performance of traditional prefabricated retaining walls. It has excellent applicability to highway slope construction and therefore can be applied in several contexts.
Some Hermite–Hadamard and Hermite–Hadamard–Fejér Type Fractional Inclusions Pertaining to Different Kinds of Generalized Preinvexities
Authors: Tariq Muhammad , Sahoo Soubhagya Kumar , Ntouyas Sotiris K. , Alsalami Omar Mutab , Shaikh Asif Ali , Nonlaopon Kamsing
Published: September 2022

Fractional derivative and integral operators are often employed to present new generalizations of mathematical inequalities. The introduction of new fractional operators has prompted another direction in different branches of mathematics and applied sciences. First, we investigate and prove new fractional equality. Considering this equality as the auxiliary result, we attain some estimations of a Hermite–Hadamard type inequality involving s-preinvex, s-Godunova–Levin preinvex, and prequasi invex functions. In addition, we investigate a fractional order Hadamard–Fejér inequality and some of its refinements pertaining to h-preinvexity via a non-conformable fractional integral operator. Finally, we present a Pachpatte type inequality for the product of two preinvex functions. The findings as well as the special cases presented in this research are new and applications of our main results.
A New Method of Simulation of Cosmic-ray Ensembles Initiated by Synchrotron Radiation
Authors: Sushchov Oleksandr , Homola Piotr , Piekarczyk Marcin , Ruimi Ophir , Almeida Cheminant Kévin , Bar Olaf , Bibrzycki Łukasz , Hnatyk Bohdan , Kovács Péter , Łozowski Bartosz , Niedźwiecki Michał , Stuglik Sławomir , Tursunov Arman , Wibig Tadeusz
Published: September 2022

Cosmic rays interact with fields and background radiation as they propagate in space, producing particle cascades of various sizes, shapes and constituents. The potential observation of at least parts of such phenomena, referred to as cosmic-ray ensembles (CRE), from Earth would open a new channel of cosmic-ray investigation, since it might be a manifestation of fundamental symmetries of nature. Research dedicated to CRE is one of the main scientific objectives of the Cosmic-Ray Extremely Distributed Observatory (CREDO) Collaboration, and with this article we address one of the cornerstones of the relevant scientific program: the simulation method dedicated to CRE studies. Here we focus on CRE resulting from synchrotron radiation by high energy electrons as one of the most prevalent energy loss processes. Providing the example of simulation output analysis, we demonstrate the advantages of our approach as well as discuss the possibility of generalization of current research.
Assessment of the Asymmetry of the Intervertebral Foramina within the Lower Motion Segments of the Lumbar Spine on the Computer Tomography Sections
Authors: Nowak Piotr , Kubaszewski Łukasz
Published: September 2022

Asymmetry in the degenerative lumbar spine may eventually evolve to debilitating scoliosis. The point where it starts to accelerate in some individuals is obscure. Discreet preclinical asymmetry may be a prognostic feature of the condition. The aim of this study was to assess the asymmetry of the intervertebral foramina of lower lumbar motion segments of the spine based on morphometric measurements using novel techniques of three-dimensional imaging. Computed tomography scans of 19 subjects with axial low back pain aged 50 to 78 of both sexes were digitally processed, and 3D reconstruction of L4/L5 and L5/S1 motion segments were obtained. The planes of intervertebral foramina were defined in reference to the axis of the pedicles bordering the intervertebral canal and the measurements were performed yielding absolute values in millimeters. The circumference ranged from 33.4 mm to 63.86 mm on the left side and from 33.01 mm to 73.54 mm on the right side. The horizontal dimension ranged from 6.86 mm to 17.84 mm on the left side and from 5.41 mm to 17.63 mm on the right side. The vertical dimension ranged from 10.17 mm to 23.65 mm on the left side and from 9.04 mm to 29.69 mm on the right side. All the measurements were combined to assess the asymmetry. Only in three cases (15%), symmetry in both segments was confirmed. No defined lateralization of asymmetry was found, as well as no significant link between the sex and age could be confirmed. This is the first study confirming the asymmetry among the intervertebral foramina. It seems that it is the dominant phenotype for intervertebral foramina. As the observed asymmetry lacks the dominant lateralization, it may be linked with the same trait of degenerative scoliosis. Though no direct link between the age and sex was confirmed in this study, it suggests other factors should be considered in prediction of the progress and magnitude of the deformity.
Cumulative Risk Meets Inter-Individual Variability: Probabilistic Concentration Addition of Complex Mixture Exposures in a Population-Based Human In Vitro Model
Authors: Jang Suji , Ford Lucie C. , Rusyn Ivan , Chiu Weihsueh A.
Published: September 2022

Although humans are continuously exposed to complex chemical mixtures in the environment, it has been extremely challenging to investigate the resulting cumulative risks and impacts. Recent studies proposed the use of “new approach methods,” in particular in vitro assays, for hazard and dose–response evaluation of mixtures. We previously found, using five human cell-based assays, that concentration addition (CA), the usual default approach to calculate cumulative risk, is mostly accurate to within an order of magnitude. Here, we extend these findings to further investigate how cell-based data can be used to quantify inter-individual variability in CA. Utilizing data from testing 42 Superfund priority chemicals separately and in 8 defined mixtures in a human cell-based population-wide in vitro model, we applied CA to predict effective concentrations for cytotoxicity for each individual, for “typical” (median) and “sensitive” (first percentile) members of the population, and for the median-to-sensitive individual ratio (defined as the toxicodynamic variability factor, TDVF). We quantified the accuracy of CA with the Loewe Additivity Index (LAI). We found that LAI varies more between different mixtures than between different individuals, and that predictions of the population median are generally more accurate than predictions for the “sensitive” individual or the TDVF. Moreover, LAI values were generally <1, indicating that the mixtures were more potent than predicted by CA. Together with our previous studies, we posit that new approach methods data from human cell-based in vitro assays, including multiple phenotypes in diverse cell types and studies in a population-wide model, can fill critical data gaps in cumulative risk assessment, but more sophisticated models of in vitro mixture additivity and bioavailability may be needed. In the meantime, because simple CA models may underestimate potency by an order of magnitude or more, either whole-mixture testing in vitro or, alternatively, more stringent benchmarks of cumulative risk indices (e.g., lower hazard index) may be needed to ensure public health protection.
Tuberculosis Disability Adjusted Life Years, Colombia 2010–2018
Authors: Plata-Casas Laura , Gutierrez-Lesmes Oscar , Cala-Vitery Favio
Published: September 2022

Estimating the burden of tuberculosis disease is relevant for assessing and identifying population health status and progress in policies and programs aimed at epidemic control. The objective of this study was to estimate disability-adjusted life years attributable to Tuberculosis in Colombia 2010–2018. A longitudinal descriptive study was conducted. The variables, sex, age groups and origin were studied. This study included 110,475 cases of morbidity and 8514 cases of mortality. Indicators of years of life lost, years of life with disability and disability-adjusted life years at the subnational level were determined using the methodology of the World Health Organization. With the results of this last indicator, an epidemiological risk stratification was carried out. The DALY rate of the study period was 684 (95% CI 581.2–809.1) per 100,000 inhabitants. According to sex, 68.4% occurred in men; for every DALY in women, 2.21 occur in men. People of productive age (15 to 69 years) account for 56% of DALYs. Amazonas (1857.1 CI 95% 1177.1–2389.6) was the territorial entity with the highest rate. A total of 51.5% of the territorial entities of departmental order of the country are of high burden for Tuberculosis. For the first time in Colombia, a comprehensive assessment of the status of the disease burden at a subnational (departmental) territorial level attributable to Tuberculosis is being carried out using the updated World Health Organization methodology. The results obtained allow us to specify that there is a knowledge gap in terms of the realization and clear understanding of the burden of the disease in Colombia. There are territorial gaps that are necessary to know in order to plan, develop, implement and redirect policies to improve health and eliminate disparities according to the territorial context.
Receptor-Binding Domain (RBD) Antibodies Contribute More to SARS-CoV-2 Neutralization When Target Cells Express High Levels of ACE2
Authors: Farrell Ariana Ghez , Dadonaite Bernadeta , Greaney Allison J. , Eguia Rachel , Loes Andrea N. , Franko Nicholas M. , Logue Jennifer , Carreño Juan Manuel , Abbad Anass , Chu Helen Y. , Matreyek Kenneth A. , Bloom Jesse D.
Published: September 2022

Neutralization assays are experimental surrogates for the effectiveness of infection- or vaccine-elicited polyclonal antibodies and therapeutic monoclonal antibodies targeting SARS-CoV-2. However, the measured neutralization can depend on the details of the experimental assay. Here, we systematically assess how ACE2 expression in target cells affects neutralization by antibodies to different spike epitopes in lentivirus pseudovirus neutralization assays. For high ACE2-expressing target cells, receptor-binding domain (RBD) antibodies account for nearly all neutralizing activity in polyclonal human sera. However, for lower ACE2-expressing target cells, antibodies targeting regions outside the RBD make a larger (although still modest) contribution to serum neutralization. These serum-level results are mirrored for monoclonal antibodies: N-terminal domain (NTD) antibodies and RBD antibodies that do not compete for ACE2 binding incompletely neutralize on high ACE2-expressing target cells, but completely neutralize on cells with lower ACE2 expression. Our results show that the ACE2 expression level in the target cells is an important experimental variable, and that high ACE2 expression emphasizes the role of a subset of RBD-directed antibodies.