bluuebunny/crossref_metadata_2025 huggingface dataset:
Using a Value Chain Approach to Map the Pig Production System in Rwanda, Its Governance, and Sanitary Risks
Authors: Shyaka Anselme , Quinnell Rupert J. , Rujeni Nadine , Fèvre Eric M.
Published: January 2022

Rwanda has a fast growing pig production sector projected to continue expansion, due to rising local and regional demand. We undertook a value chain analysis to establish the flows of pigs and pork in Rwanda and the roles of various actors involved, and to understand governance and sanitary risks in the value chain. Cross-sectional qualitative data were collected through focus group discussions and key informant interviews with farmers, brokers, butchers, abattoir managers, and veterinarians. Data were collected on pig production methods and inputs, the source and destination of live and slaughtered pigs, value-adding infrastructures (abattoirs and processing factories), the people involved and interactions between them, governance, and challenges. Pig production in Rwanda is dominated by smallholders, mainly as a source of supplementary income and secondarily for manure. Emerging medium-sized and large pig farms were also identified, located mainly around urban areas. Live pig markets are the main mechanism allowing various actors to buy/sell pigs. Brokers have an important role in pig transactions: they are key in setting prices at markets, examining pigs for disease, organising the supply of pigs for abattoirs and for export. Only a few formal pig abattoirs were identified, which mainly supply to pork processing factories based in Kigali and/or export to customers. Local consumers rely on informal slaughtering at farm or bar/restaurant backyards, with irregular veterinary inspection. Formal abattoirs were attended by a veterinary inspector, however a lack of record keeping was noted. Sanitary risks identified were a lack of biosecurity throughout the chain and poor hygiene at slaughter places. Lingual palpation was practised in pig markets to identify cysticercosis infection, however cyst-positive pigs were not destroyed, but were sold for reduced prices in the same market or later informally sold by the owner. There are few veterinarians attending farms, with most services provided by less qualified technicians or self-treatment of pigs by farmers. Overall, this production system is characterised by a high degree of informality at all nodes, combined with the rapid growth trajectory in the sector. These findings provide a basis to plan interventions tailored to vulnerabilities identified in the Rwanda pig value chain.
Prophylactic Effects of Ivermectin and Closantel Treatment in the Control of Oestrus ovis Infestation in Sheep
Authors: Bello Hornblenda Joaquina Silva , Lins José Gabriel Gonçalves , Albuquerque Ana Cláudia Alexandre de , Ferreira Gabriel Badial , Amarante Mônica Regina Vendrame , Amarante Alessandro Francisco Talamini do
Published: January 2022

The sheep nasal bots Oestrus ovis is parasite of the nasal cavities and sinuses of small ruminants causing oestrosis, one of the most frequent parasitic diseases in sheep and goats. The widely use of ivermectin and closantel by the sheep breeders in the treatment and prophylaxis of gastrointestinal nematodes resulted in widespread cases of anthelmintic resistance. However, there is no report about cases of O. ovis with drug-resistance. In this study, we evaluated the prophylactics and therapeutic effects of both antiparasitics in sheep with O. ovis natural infestation. The trial was carried out from early December 2019 to March 2020, with 30 crossbred males lambs allocated into three groups of 10 animals each: control (without treatment), treated with ivermectin (0.2 mg/kg subcutaneously) and treated with closantel (10 mg/kg orally). The animals were kept together grazing the same pasture area. The treatment groups were drenched in two occasions 70 days apart: on 5th December 2019 and on 13th February 2020. On 19th March 2020, all lambs were slaughtered. The lamb heads were removed and sectioned along their longitudinal and sagittal axis to search for larvae. Recovered O. ovis larvae were counted and identified according to their developmental stage (L1, L2, and L3). Seven of the control lambs were infested with O. ovis larvae ranging from six to 17 larvae (11.6 mean infestation intensity). All recovered larvae from control group were intact and active. Three animals treated with ivermectin had O. ovis larvae (1–3 larvae), however they were dead and in degeneration. The animals treated with closantel did not have any larvae. The clinical suggestive signs of oestrosis were scarce over the experimental period. The averages of daily weight gain were similar (p > 0.05) among groups. Closantel and ivermectin had high efficacy against oestrosis and O. ovis parasitism did not hinder the performance of lambs.
Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
Authors: Hoffmann Maximilian , Bergmann Ralph
Published: January 2022

Similarity-based retrieval of semantic graphs is a core task of Process-Oriented Case-Based Reasoning (POCBR) with applications in real-world scenarios, e.g., in smart manufacturing. The involved similarity computation is usually complex and time-consuming, as it requires some kind of inexact graph matching. To tackle these problems, we present an approach to modeling similarity measures based on embedding semantic graphs via Graph Neural Networks (GNNs). Therefore, we first examine how arbitrary semantic graphs, including node and edge types and their knowledge-rich semantic annotations, can be encoded in a numeric format that is usable by GNNs. Given this, the architecture of two generic graph embedding models from the literature is adapted to enable their usage as a similarity measure for similarity-based retrieval. Thereby, one of the two models is more optimized towards fast similarity prediction, while the other model is optimized towards knowledge-intensive, more expressive predictions. The evaluation examines the quality and performance of these models in preselecting retrieval candidates and in approximating the ground-truth similarities of a graph-matching-based similarity measure for two semantic graph domains. The results show the great potential of the approach for use in a retrieval scenario, either as a preselection model or as an approximation of a graph similarity measure.
Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
Authors: Jiang Nianqi , Bai Fenhua , Huang Lin , An Zhengyuan , Shen Tao
Published: January 2022

The Internet of Things that links the cyber and physical worlds brings revolutionary changes to society, however, its security and efficiency problems have not been solved. The Consortium Blockchain + IoT is considered to be an effective solution. The IoT blockchain network’s demand for transaction processing speed is gradually increasing. The throughput problem of the blockchain needs to be solved urgently and the security problem of transaction processing that comes with it. To solve the above problems, this paper proposes a reputation-driven dynamic node security sharding consensus model (RDSCM) in the blockchain, which consists of two parts: a reputation-driven node to eliminate PBFT (RE-PBFT) and a reputation-driven node cross reconfiguration sharding scheme (NCRS). The RE-PBFT eliminates abnormal nodes in the consensus network and reduces the probability of abnormal nodes becoming master nodes. NCRS improves the blockchain throughput while ensuring sharding reliability. Finally, the experiment proves that RE-PBFT can identify abnormal nodes and remove them in a short time. NCRS can effectively guarantee the reliability of sharding, and the transaction processing efficiency has been greatly improved after sharding.
A Spacecraft Attitude Determination and Control Algorithm for Solar Arrays Pointing Leveraging Sun Angle and Angular Rates Measurements
Authors: Colagrossi Andrea , Lavagna Michèle
Published: January 2022

The capability to orient the solar arrays of a spacecraft toward the Sun is an ultimate asset for any attitude determination and control subsystem (ADCS). This ability should be maintained in any operative circumstance, either nominal or off-nominal, to avoid the loss of the entire space-borne system. The safe mode implementation should guarantee a positive power generation from the solar arrays, regardless of the health status of the satellite platform. This paper presents a solar array pointing algorithm, to be executed on-board, with a minimal set of sensors and actuators. In fact, the sensors are limited to the solar arrays, exploiting the current/voltage sensing capacity of the electrical power subsystem to measure the Sun angle with respect to the arrays normal, and to the angular rates sensors. The actuators are required to provide a torque only along two axes and, thus, a reduced actuation capacity is still manageable by the proposed algorithm. The paper describes the algorithm, both in the Sun direction determination and in the Sun pointing control capacity. The achieved performance is outlined, considering either an ideal system or a realistic one, being the latter affected by sensors and actuators limitations. The actuation by means of momentum exchange devices or magnetic torquers is discussed, with the purpose to prove the wide applicability range of the presented algorithm, which is capable to guarantee solar array orientation with a minimal hardware set.
Variations in the Antioxidant, Anticancer, and Anti-Inflammatory Properties of Different Rosa rugosa Organ Extracts
Authors: Kim Eunhui , Mok Hae Kyung , Hyun Tae Kyung
Published: January 2022

Rosa rugosa is widely used as a health food and medicine due to its broad pharmacological properties. Although the bioactivities obtained from plant materials is related to the type and amount of phytochemicals in each extract, no systematic information is available on the organ-dependent bioactivities of R. rugosa. Here, the antioxidant, anticancer, and anti-inflammatory activities of R. rugosa stem, leaf, flower, and fruit ethanol extracts were evaluated. Overall, the stem extract exhibited the highest levels of DPPH radical scavenging activity, ferric reducing power, and oxygen radical antioxidant capacity compared with other organ extracts, whereas leaves contained potent anticancer compounds that were particularly effective against A549 cells. Additionally, the leaf extract inhibited the MEK/ERK signaling pathway, resulting in the transcriptional repression of pro-inflammatory mediators in LPS-stimulated RAW 264.7 cells. Furthermore, significant correlation between phytochemical content and bioactivities indicated that phenolic compounds play as a major antioxidant compound of R. rugosa. Taken together, these findings suggested that the spatial distribution of the phytochemicals contributed to the biological activities of R. rugosa. Given that R. rugosa fruits and flowers are already being used in health foods and medicine, these results indicate that the leaves and stems of R. rugosa should also be included and used as natural sources of antioxidant, anticancer, and anti-inflammatory agents.
Linking Animal Welfare and Antibiotic Use in Pig Farming—A Review
Authors: Albernaz-Gonçalves Rita , Olmos Antillón Gabriela , Hötzel Maria José
Published: January 2022

Preventative measures, such as biosecurity and vaccinations, are essential but not sufficient to ensure high standards of health in pig production systems. Restrictive, barren housing and many widely used management practices that cause pain and stress predispose high-performance pigs reared in intensive systems to disease. In this context, antibiotics are used as part of the infrastructure that sustains health and high levels of production in pig farms. Antimicrobial resistance (AMR) is a global emergency affecting human and animal health, and the use of antibiotics (AMU) in intensive livestock farming is considered an important risk factor for the emergence and spread of resistant bacteria from animals to humans. Tackling the issue of AMR demands profound changes in AMU, e.g., reducing their use for prophylaxis and ending it for growth promotion. In support of such recommendations, we revise the link between animal welfare and AMU and argue that it is crucial to sustainably reduce AMU while ensuring that pigs can live happy lives. In support of such recommendations, we aimed to revise the link between animal welfare and AMU in pigs by analysing stress factors related to housing and management and their impact on pig welfare. In particular, we reviewed critical management practices that increase stress and, therefore, pigs’ susceptibility to disease and reduce the quality of life of pigs. We also reviewed some alternatives that can be adopted in pig farms to improve animal welfare and that go beyond the reduction in stress. By minimising environmental and management stressors, pigs can become more immunocompetent and prepared to overcome pathogenic challenges. This outcome can contribute to reducing AMU and the risk of AMR while simultaneously improving the quality of life of pigs and, ultimately, maintaining the pig industry’s social license.
Identification of Copy Number Variations and Genetic Diversity in Italian Insular Sheep Breeds
Authors: Di Gerlando Rosalia , Mastrangelo Salvatore , Tolone Marco , Rizzuto Ilaria , Sutera Anna Maria , Moscarelli Angelo , Portolano Baldassare , Sardina Maria Teresa
Published: January 2022

Copy number variants (CNVs) are one of the major contributors to genetic diversity and phenotypic variation in livestock. The aim of this work is to identify CNVs and perform, for the first time, a CNV-based population genetics analysis with five Italian sheep breeds (Barbaresca, Comisana, Pinzirita, Sarda, and Valle del Belìce). We identified 10,207 CNVs with an average length of 1.81 Mb. The breeds showed similar mean numbers of CNVs, ranging from 20 (Sarda) to 27 (Comisana). A total of 365 CNV regions (CNVRs) were determined. The length of the CNVRs varied among breeds from 2.4 Mb to 124.1 Mb. The highest number of shared CNVRs was between Comisana and Pinzirita, and only one CNVR was shared among all breeds. Our results indicated that segregating CNVs expresses a certain degree of diversity across all breeds. Despite the low/moderate genetic differentiation among breeds, the different approaches used to disclose the genetic relationship showed that the five breeds tend to cluster in distinct groups, similar to the previous studies based on single-nucleotide polymorphism markers. Gene enrichment was described for the 37 CNVRs selected, considering the top 10%. Out of 181 total genes, 67 were uncharacterized loci. Gene Ontology analysis showed that several of these genes are involved in lipid metabolism, immune response, and the olfactory pathway. Our results corroborated previous studies and showed that CNVs represent valuable molecular resources for providing useful information for separating the population and could be further used to explore the function and evolutionary aspect of sheep genome.
Evaluation of Maternal Genetic Background of Two Hungarian Autochthonous Sheep Breeds Coming from Different Geographical Directions
Authors: Gáspárdy András , Zenke Petra , Kovács Endre , Annus Kata , Posta János , Sáfár László , Maróti-Agóts Ákos
Published: January 2022

The aim of our research was the evaluation of the maternal genetic background of two Hungarian autochthonous sheep breeds of different geographical origin. A major argument for the preservation of endangered animal breeds is their documented past and historical importance. These also include the registration of pedigree data. This is the first study to evaluate and compare Tsigai and Cikta sheep in Hungary. Our investigation is based on two complete sequences of mitochondrial DNA (cytochrome b gene and control region). Our research was performed on these two sheep breeds with markedly different breed histories and breed characteristics to determine a possible common maternal genetic background, as ultimately the origin of both breeds can be traced back to Asia Minor. Between 2015 and 2017, a total of 203 biological samples were taken using a newly introduced founder sampling method. We found that the prevailing haplogroup B accounted for over 80% of both breeds, strengthening the common ancestral root. However, the pairwise genetic differentiation estimates (KST) calculated using the sequence-based statistics for cytochrome b gene and control region were 0.034 and 0.021, respectively (both at level p < 0.05); thus, revealing genetic differentiation in both sequences between the Tsigai and Cikta. We note that the known different history of the breeds is clearly justified by the currently studied deviations in their maternal genetic background.
Sensor-Based Detection of Predator Influence on Livestock: A Case Study Exploring the Impacts of Wild Dogs (Canis familiaris) on Rangeland Sheep
Authors: Evans Caitlin A. , Trotter Mark G. , Manning Jaime K.
Published: January 2022

In Australia, wild dogs are one of the leading causes of sheep losses. A major problem with managing wild dogs in Australia’s rangeland environments is that sheep producers are often unaware of their presence until injuries or deaths are observed. One option for earlier detection of wild dogs is on-animal sensors, such as Global Positioning System (GPS) tracking collars, to detect changes in the behaviour of sheep due to the presence of wild dogs. The current study used spatio-temporal data, derived from GPS tracking collars, deployed on sheep from a single rangeland property to determine if there were differences in the behaviour of sheep when in the presence, or absence, of a wild dog. Results indicated that the presence of a wild dog influenced the daily behaviours of sheep by increasing the daily distance travelled. Differences in sheep diurnal activity were also observed during periods where a wild dog was present or absent on the property. These results highlight the potential for on-animal sensors to be used as a monitoring tool for sheep flocks directly impacted by wild dogs, although further work is needed to determine the applicability of these results to other sheep production regions of Australia.
Genetic Diversity, Population Structure and Phylogeny of Indigenous Goats of Mongolia Revealed by SNP Genotyping
Authors: Mukhina Vera , Svishcheva Gulnara , Voronkova Valery , Stolpovsky Yurii , Piskunov Aleksei
Published: January 2022

Mongolian goats are of great interest for studying ancient migration routes and domestication, and also represent a good model of adaptability to harsh environments. Recent climatic disasters and uncontrolled massive breeding endangered the valuable genetic resources of Mongolian goats and raised the question of their conservation status. Meanwhile, Mongolian goats have never been studied on genomic scale. We used Illumina Goat SNP50 to estimate genetic risks in five Mongolian goat breeds (Buural, Ulgii Red, Gobi GS, Erchim, Dorgon) and explored phylogenic relationships among these populations and in the context of other breeds. Various clustering methods showed that Mongolian goats grouped with other Asian breeds and were especially close to some neighboring Russian and Chinese breeds. The Buural breed showed the lowest estimates of inbreeding and exhibited the shortest genetic distances within the other Mongolian breeds, especially, to Ulgii Red and Gobi GS. These three breeds formed a single core group, being weakly differentiated from each other. Among them, Gobi GS displayed obvious signs of inbreeding probably resulted from artificial selection pressure. Dorgon and especially Erchim goats stand apart from the other Mongolian breeds according to various types of analyses, and bear unique features pointing to different breeding histories or distinct origins of these breeds. All populations showed strong decline in effective population size. However, none of them met formal criteria to be considered as endangered breeds. The SNP data obtained in this study improved the knowledge of Mongolian goat breeds and could be used in future management decisions in order to preserve their genetic diversity.
Development and Progression of Shoulder Lesions and Their Influence on Sow Behavior
Authors: Gaab Tara , Nogay Emily , Pierdon Meghann
Published: January 2022

Shoulder lesions can develop in sows during lactation and vary in severity, potentially leading to euthanasia of the sow. There are questions about how these lesions affect the sow’s welfare. Here, sows that were loaded into farrowing pens were monitored prospectively to elucidate the risk for lesion development. To determine whether the presence of shoulder lesions affected behavior, 44 sows with shoulder lesions (LES) were matched to controls (CON) and observed during farrowing for postures and during nursing and gestation for posture and location. Lesions were measured daily. A low weaning body condition score (BCS) increased the possibility of lesion development (OR = 4.8 ± 2.8; p = 0.01). There was no difference in behavior between LES and CON with the exception of a higher frequency of postural changes in CON sows (p = 0.01). A larger maximum lesion size was associated with larger initial lesion (p < 0.01), higher weaning BCS (p < 0.001), low parity (p < 0.001), and lameness (p < 0.001). Median time to healing (24 ± 2.2 days) correlated with maximum lesion size. A low BCS during weaning increased the risk for lesion development and there were multiple factors found which influenced maximum lesion size; however, we did not find behavioral indications that lesions impacted welfare.
Effect of Grape Marc Added Diet on Live Weight Gain, Blood Parameters, Nitrogen Excretion, and Behaviour of Sheep
Authors: Wu Huichu , Zhang Pangzhen , Zhang Fan , Shishir Md Safiqur Rahaman , Chauhan Surinder S. , Rugoho Innocent , Suleria Hafiz , Zhao Guangyong , Cullen Brendan , Cheng Long
Published: January 2022

A 39-day field experiment was conducted to assess the effect of grape marc (GM) feeding on sheep productivity, health, and environmental sustainability. Forty merino sheep were divided into two dietary groups, each having five replications (n = 4 sheep/replication). Experimental diet consisted of: (i) control: 55% lucerne hay + 40% wheat grain + 5% faba bean; (ii) GM treatment: control diet with 20% replaced by GM on a dry matter (DM) basis. The GM treatment contained 2–10% higher phytochemical contents than the control. The DMI from the GM treatment was 15% higher than the control (p < 0.001). No difference was found in sheep live weight gain, behaviour, and quality between groups (p > 0.05). No difference was found in total faecal production, faecal organic matter, and nitrogen contents (p > 0.05) and parasitic egg count. The GM treatment led to higher nitrogen intake (23.1 vs. 27.2 g/d) and faecal nitrogen excretion (6.3 vs. 8.7 g/d) compared to the control. Urinary creatinine, allantoin, and purine derivatives were lower in the GM treatment than control (p < 0.05). However, both groups had similar purine derivatives/DMI (i.e., indicator of rumen microbial protein synthesis efficiency; p > 0.05). Overall, the results showed that GM can replace 20% of the control ration to maintain sheep productivity, health, and environmental sustainability.
Analysis of Antimicrobial Use and the Presence of Antimicrobial-Resistant Bacteria on Austrian Dairy Farms—A Pilot Study
Authors: Firth Clair , Käsbohrer Annemarie , Pless Peter , Koeberl-Jelovcan Sandra , Obritzhauser Walter
Published: January 2022

The assumed link between high levels of antimicrobial use on farms and selection for antimicrobial-resistant (AMR) bacteria on that farm remains difficult to prove. In the pilot study presented here, we analysed total antimicrobial use on 50 dairy farms in Austria and also collected environmental samples to ascertain whether specific AMR bacteria were present. Antimicrobial use (AMU) analysis was based on electronic veterinary treatment records over a one-year period. Faecal samples for the assessment of extended-spectrum beta-lactamase (ESBL)-producing E. coli were collected from cowsheds, calf pens, and youngstock housing areas, as well as dust samples from barns, to isolate methicillin-resistant Staphylococcus aureus (MRSA). Bacteriological cultures were carried out on selective agar. Farms were split into groups of 25 of the highest antimicrobial users and 25 of the lowest users. Overall, samples from 13/50 (26.0%) farms were found to be positive for the presence of ESBL-producing E. coli. Of these, eight farms were in the low user group and five were in the high user group. Only one farm was confirmed to harbour MRSA. Statistical analyses demonstrated that there was no significant difference in this study population between high or low antimicrobial use with respect to the presence of ESBL-producing E. coli on farms (p = 0.33). In conclusion, the presence of specific AMR bacteria on farms in this study population was not found to have a statistically proven relationship with their level of antimicrobial use.
Comparative Transcriptome Analysis Reveals Differentially Expressed Genes Related to Antimicrobial Properties of Lysostaphin in Staphylococcus aureus
Authors: Yan Xianghe , Xie Yanping , Li Charles , Donovan David M. , Gehring Andrew , Irwin Peter , He Yiping
Published: January 2022

Comparative transcriptome analysis and de novo short-read assembly of S. aureus Newman strains revealed significant transcriptional changes in response to the exposure to triple-acting staphylolytic peptidoglycan hydrolase (PGH) 1801. Most altered transcriptions were associated with the membrane, cell wall, and related genes, including amidase, peptidase, holin, and phospholipase D/transphosphatidylase. The differential expression of genes obtained from RNA-seq was confirmed by reverse transcription quantitative PCR. Moreover, some of these gene expression changes were consistent with the observed structural perturbations at the DNA and RNA levels. These structural changes in the genes encoding membrane/cell surface proteins and altered gene expressions are the candidates for resistance to these novel antimicrobials. The findings in this study could provide insight into the design of new antimicrobial agents.
Combining Yoga Exercise with Rehabilitation Improves Balance and Depression in Patients with Chronic Stroke: A Controlled Trial
Authors: Lai Yen-Ting , Lin Chien-Hung , Hsieh City C. , Yang Jung-Cheng , Tsou Han-Hsing , Lin Chih-Ching , Li Szu-Yuan , Chan Hsiang-Lin , Liu Wen-Sheng
Published: January 2022

Background: We combined yoga with standard stroke rehabilitation and compared it to the rehabilitation alone for depression and balance in patients. Methods: Forty patients aged from 30 to 80 who had suffered a stroke 90 or more days previously were divided evenly with age stratification and patients’ will (hence not randomized). In the intervention group 16 completed 8-week stroke rehabilitation combined with 1 h of yoga twice weekly. Another 19 patients completed the standard rehabilitation as the control group. Results: The yoga group showed significant improvement in depression (Taiwanese Depression Questionnaire, p = 0.002) and balance (Berg Balance Scale, p < 0.001). However, the control group showed improvement only in balance (p = 0.001) but not in depression (p = 0.181). Further analysis showed both sexes benefitted in depression, but men had a greater improvement in balance than women. Depression in left-brain lesion patients improved more significantly than in those with right-brain lesion, whereas balance improved equally despite lesion site. For patients under or above the age of 60, depression and balance both significantly improved after rehabilitation. Older age is significantly related to poor balance but not depression. Conclusions: Combining yoga with rehabilitation has the potential to improve depression and balance. Factors related to sex, brain lesion site and age may influence the differences.
A Novel Narrowband Active Noise Control System with Online Secondary Path Modeling Based on Factor Decomposition and Application in Open Space
Authors: Li Yinsheng , Luo Zhengqiang , Xu Qing , Zheng Wei
Published: January 2022

Due to the complexity of the coupling between the active noise control (ANC) controller and secondary path estimator, performance analysis of the system becomes particularly difficult. At present, the performance analysis of the system is often based on the fact that the secondary path tends to be stable, and the secondary path fitting error is minimal. However, in the early stage of system operation, or when the secondary path changes suddenly, the secondary path fitting error is significant, which easily causes divergence of the system control. It is still unable to guarantee the step-size bounds of convergence stability. Therefore, factor decomposition was used to analyze the mean weight behavior in this study. This strategy emphasizes the influence of secondary path modeling (SPM) error. The mean square behavior was evaluated using the energy conservation relationship. According to the established theoretical model, the convergence condition of the system was derived and the upper bound of step size suitable for all stages of system operation was obtained. The simulation and experimental results show that the ANC system is quite stable and robust under extreme conditions and has an obvious noise reduction effect in a specific range of open space, which can reach about 20 dB noise reduction.
A CSI-Based Multi-Environment Human Activity Recognition Framework
Authors: Alsaify Baha A. , Almazari Mahmoud M. , Alazrai Rami , Alouneh Sahel , Daoud Mohammad I.
Published: January 2022

Passive human activity recognition (HAR) systems, in which no sensors are attached to the subject, provide great potentials compared to conventional systems. One of the recently used techniques showing tremendous potential is channel state information (CSI)-based HAR systems. In this work, we present a multi-environment human activity recognition system based on observing the changes in the CSI values of the exchanged wireless packets carried by OFDM subcarriers. In essence, we introduce a five-stage CSI-based human activity recognition approach. First, the acquired CSI values associated with each recorded activity instance are processed to remove the existing noise from the recorded data. A novel segmentation algorithm is then presented to identify and extract the portion of the signal that contains the activity. Next, the extracted activity segment is processed using the procedure proposed in the first stage. After that, the relevant features are extracted, and the important features are selected. Finally, the selected features are used to train a support vector machine (SVM) classifier to identify the different performed activities. To validate the performance of the proposed approach, we collected data in two different environments. In each of the environments, several activities were performed by multiple subjects. The performed experiments showed that our proposed approach achieved an average activity recognition accuracy of 91.27%.
Human Activity Classification Using the 3DCNN Architecture
Authors: Vrskova Roberta , Hudec Robert , Kamencay Patrik , Sykora Peter
Published: January 2022

Interest in utilizing neural networks in a variety of scientific and academic studies and in industrial applications is increasing. In addition to the growing interest in neural networks, there is also a rising interest in video classification. Object detection from an image is used as a tool for various applications and is the basis for video classification. Identifying objects in videos is more difficult than for single images, as the information in videos has a time continuity constraint. Common neural networks such as ConvLSTM (Convolutional Long Short-Term Memory) and 3DCNN (3D Convolutional Neural Network), as well as many others, have been used to detect objects from video. Here, we propose a 3DCNN for the detection of human activity from video data. The experimental results show that the optimized proposed 3DCNN provides better results than neural network architectures for motion, static and hybrid features. The proposed 3DCNN obtains the highest recognition precision of the methods considered, 87.4%. In contrast, the neural network architectures for motion, static and hybrid features achieve precisions of 65.4%, 63.1% and 71.2%, respectively. We also compare results with previous research. Previous 3DCNN architecture on database UCF Youtube Action worked worse than the architecture we proposed in this article, where the achieved result was 29%. The experimental results on the UCF YouTube Action dataset demonstrate the effectiveness of the proposed 3DCNN for recognition of human activity. For a more complex comparison of the proposed neural network, the modified UCF101 dataset, full UCF50 dataset and full UCF101 dataset were compared. An overall precision of 82.7% using modified UCF101 dataset was obtained. On the other hand, the precision using full UCF50 dataset and full UCF101 dataset was 80.6% and 78.5%, respectively.
A Bus Passenger Flow Prediction Model Fused with Point-of-Interest Data Based on Extreme Gradient Boosting
Authors: Lv Wanjun , Lv Yongbo , Ouyang Qi , Ren Yuan
Published: January 2022

Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of urban bus passenger flow has become one of the research hotspots in the field of public transportation, especially with the rise of big data technology. Bus IC card data are an important data resource and are more valuable to passenger flow prediction in comparison with manual survey data. Aiming at the balance between efficiency and accuracy of passenger flow prediction for multiple lines, we propose a novel passenger flow prediction model based on the point-of-interest (POI) data and extreme gradient boosting (XGBoost), called PFP-XPOI. Firstly, we collected POI data around bus stops based on the Amap Web service application interface. Secondly, three dimensions were considered for building the model. Finally, the XGBoost algorithm was chosen to train the model for each bus line. Results show that the model has higher prediction accuracy through comparison with other models, and thus this method can be used for short-term passenger flow forecasting using bus IC cards. It plays a very important role in providing decision basis for more refined bus operation management.
Adaptive Metaheuristic-Based Methods for Autonomous Robot Path Planning: Sustainable Agricultural Applications
Authors: Kiani Farzad , Seyyedabbasi Amir , Nematzadeh Sajjad , Candan Fuat , Çevik Taner , Anka Fateme Aysin , Randazzo Giovanni , Lanza Stefania , Muzirafuti Anselme
Published: January 2022

The increasing need for food in recent years means that environmental protection and sustainable agriculture are necessary. For this, smart agricultural systems and autonomous robots have become widespread. One of the most significant and persistent problems related to robots is 3D path planning, which is an NP-hard problem, for mobile robots. In this paper, efficient methods are proposed by two metaheuristic algorithms (Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO)). The proposed methods try to find collision-free optimal paths between two points for robots without human intervention in an acceptable time with the lowest process costs and efficient use of resources in large-scale and crowded farmlands. Thanks to the methods proposed in this study, various tasks such as tracking crops can be performed efficiently by autonomous robots. The simulations are carried out using three methods, and the obtained results are compared with each other and analyzed. The relevant results show that in the proposed methods, the mobile robots avoid the obstacles successfully and obtain the optimal path cost from source to destination. According to the simulation results, the proposed method based on the Ex-GWO algorithm has a better success rate of 55.56% in optimal path cost.
Improved Interaction of BIM Models for Historic Buildings with a Game Engine Platform
Authors: Ma Yu-Pin
Published: January 2022

For the purpose of the conservation and representation of cultural heritage, the development of a visual interactive environment to provide an integrated application suitable for collecting a wide range of big data information from historic blocks is regarded as an effective solution. In this study, the existing modeling of wooden building information is imported into a game engine as a practical study for the design of a user-friendly interactive interface design, a Unity integration platform is built, a Revit external application is developed, and cloud services architecture is set to solve the transmission and integration of information models between different platforms. In addition to strengthening the future application of historic building information modeling, this visual information-based interactive environment can help enhance communication and subsequent management of repairs, and bring opportunities for sustainable management and diversified applications to the research field of historic building restoration.
A Numerical Simulation of Radiation Chemistry for Controlling the Oxidising Environment in Water-Cooled Nuclear Power Reactors
Authors: Swiatla-Wojcik Dorota
Published: January 2022

Maintaining the integrity of materials of light-water nuclear power reactors requires the development of effective methods to control and minimise the corrosive environment associated with the radiolysis of a coolant. In this study, the behaviour of the oxidising environment is simulated using a hybrid method. The hybrid method has advantages in that the production of radiolytic species under exposure of the coolant to ionising radiation is simulated while providing material and charge balances. Steady-state concentrations of stable and transient oxidising agents are calculated as a function of radiation composition and dose rate by numerical integration of the system of kinetic equations describing radiation chemistry of neutral water, the alkaline solution, and the hydrogenated systems at 300 °C. The importance of the reactions and equilibria constituting the radiolysis scheme of the coolant is assessed. The influence of the presence of a base and the injected H2 on the yield of key reactions responsible for the formation of the main oxidants H2O2 and O2 are discussed. Simulation indicated the synergic effect of H2 gas and base added to the coolant on diminishment of the steady-state concentration of oxidants.
Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes
Authors: Salem Hanaa , Shams Mahmoud Y. , Elzeki Omar M. , Abd Elfattah Mohamed , F. Al-Amri Jehad , Elnazer Shaima
Published: January 2022

Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity.
High-Throughput Online Visual Detection Method of Cracked Preserved Eggs Based on Deep Learning
Authors: Tang Wenquan , Hu Jianchao , Wang Qiaohua
Published: January 2022

Cracked preserved eggs can easily decay, emit a peculiar smell, and cause cross-infection. The identification of cracked preserved eggs during production suffers from low efficiency and high cost. This paper proposes an online detection and identification method of cracked preserved eggs to address this issue. First, the images of preserved eggs are collected online. Then, each collected image is cut into a single image of the preserved egg, and the images of different surfaces of the same preserved egg are respectively spliced by the sequential splicing scheme and the matrix splicing scheme. Finally, the data sets obtained by the two stitching methods are exploited to establish a deep learning detection model. The experimental results indicate that the MobileNetV3_egg model, an improved version of the MobileNetV3_large model, achieves the best recognition ability for cracked preserved eggs by using the matrix splicing scheme. The accuracy reaches 96.3%, and the detection time for 300 images is only 4.267 s. The proposed method can meet the needs of actual production, and the application of this method will make the identification of cracked preserved eggs more automated and intelligent.
Saturation, Allowed Transitions and Quantum Interference in Laser Cooling of Solids
Authors: Andre Laura B. , Cheng Long , Rand Stephen C.
Published: January 2022

New methods for the rapid cooling of solids with increased efficiency are analyzed and demonstrated experimentally. The advances offered by optical saturation, dipole-allowed transitions, and quantum interference for improved laser cooling of solids are highlighted.
Effect of Sodium on Methanogens in a Two-Stage Anaerobic System
Authors: Xiao Keke , Maspolim Yogananda , Zhou Yan , Guo Chenghong , Ng Wun Jern
Published: January 2022

This study evaluated the effects of sodium on anaerobic biomass from the second-stage reactor of a two-stage anaerobic digester. The results indicated that methanogens showed a relatively high sodium tolerance of 2.4 g Na+ L−1. Microbial community analysis showed that viable Methanomicrobiales was the most abundant population by a combined propidium monoazide cross-linking quantitative polymerase chain reaction technique. There was a population shift towards higher abundance of Thermotoga (0.02%), Clostridium (2.50%) and Methanoculleus (13.80%). Biomass activity in relation to increased sodium concentrations was investigated with the adenosine triphosphate test coupled with extracellular polymeric substances measurement. The results showed biomass activity decreased from 33 to 16 µg g−1 volatile suspended solids as sodium concentrations increased from 1.3 to 9.1 g Na+ L−1. Higher EPS production, particularly a greater predominance of carbohydrates, was stimulated by higher sodium concentrations. This study provides insights into the superiority of sodium tolerance of two-stage anaerobic digester in compared with a single-stage anaerobic system.
Sulfonated Polyether Ether Ketone and Organosilica Layered Nanofiller for Sustainable Proton Exchange Membranes Fuel Cells (PEMFCs)
Authors: Lufrano Ernestino , Simari Cataldo , Enotiadis Apostolos , Nicotera Isabella
Published: January 2022

The ease and low environmental impact of its preparation, the reduced fuel crossover, and the low cost, make sulfonated polyether ether ketone (sPEEK) a potential candidate to replace the Nafion ionomer in proton exchange membrane fuel cells (PEMFCs). In this study, sPEEK was used as a polymer matrix for the preparation of nanocomposite electrolyte membranes by dispersing an organo-silica layered material properly functionalized by anchoring high phosphonated (PO3H) ionic groups (nominated PSLM). sPEEK-PSLM membranes were prepared by the solution intercalation method and the proton transport properties were investigated by NMR (diffusometry-PFG and relaxometry-T1) and EIS spectroscopies, whereas the mechanical properties of the membranes were studied by dynamic mechanical analysis (DMA). The presence of the organosilica nanoplatelets remarkably improved the mechanical strength, the water retention capacity at high temperatures, and the proton transport, in particular under harsh operative conditions (above 100 °C and 20–30% RH), usually required in PEMFCs applications.
A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
Authors: Ibrahim Rania A. , Zakzouk Nahla E.
Published: January 2022

Low-voltage ride-through (LVRT) and grid support capability are becoming a necessity for grid-tied renewable energy sources to guarantee utility availability, quality and reliability. In this paper, a swap control scheme is proposed for grid-tied permanent magnet synchronous generator (PMSG) MW-level wind turbines. This scheme shifts system operation from maximum power point tracking (MPPT) mode to LVRT mode, during utility voltage sags. In this mode, the rectifier-boost machine-side converter overtakes DC-link voltage regulation independently of the grid-side converter. The latter attains grid synchronization by controlling active power injection into the grid to agree with grid current limits while supporting reactive power injection according to the sag depth. Thus grid code requirements are met and power converters safety is guaranteed. Moreover, the proposed approach uses the turbine-generator rotor inertia to store surplus energy during grid voltage dips; thus, there is no need for extra hardware storage devices. This proposed solution is applied on a converter topology featuring a minimal number of active switches, compared to the popular back-to-back converter topology. This adds to system compatibility, reducing its size, cost and switching losses. Simulation and experimental results are presented to validate the proposed approach during normal and LVRT operation.