Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the Impact of Mass Testing to Transition from Pandemic Mitigation to Endemic COVID-19

As countries transition from pandemic mitigation to endemic COVID-19, mass testing may blunt the impact on the healthcare system of the liminal wave. We used GeoDEMOS-R, an agent-based model of Singapore’s population with demographic distributions and vaccination status. A 250-day COVID-19 Delta variant model was run at varying maximal rapid antigen test sensitivities and frequencies. Without testing, the number of infections reached 1,021,000 (899,400–1,147,000) at 250 days. When conducting fortnightly and weekly mass routine rapid antigen testing 30 days into the outbreak at a maximal test sensitivity of 0.6, this was reduced by 12.8% (11.3–14.5%) and 25.2% (22.5–28.5%). An increase in maximal test sensitivity of 0.2 results a corresponding reduction of 17.5% (15.5–20.2%) and 34.4% (30.5–39.1%). Within the maximal test sensitivity range of 0.6–0.8, test frequency has a greater impact than maximal test sensitivity with an average reduction of 2.2% in infections for each day removed between tests in comparison to a 0.43% average reduction per 1% increase in test frequency. Our findings highlight that mass testing using rapid diagnostic tests can be used as an effective intervention for countries transitioning from pandemic mitigation to endemic COVID-19.
The association between financial distress, conflict management, and co-parenting support for couples receiving child welfare

This study examined financial distress, conflict management, and co-parenting support among 254 couples receiving child welfare services. Using an actor-partner interdependence model, this study tested the indirect association between couples’ financial distress and their perceptions of co-parenting support through the use of positive conflict management strategies. Indirect actor effects were found for both mothers and fathers such that financial distress was associated with reports of less positive conflict management behaviors, and in turn less co-parenting support. When mothers reported that they and their partner engaged in less positive conflict management behaviors, they were more likely to be perceived as less supportive by their co-parent. Model results did not differ for married versus unmarried couples. For couples experiencing financial distress, findings imply that how effectively couples experiencing financial distress manage conflict in their relationship could potentially spillover and influence how well they support each other in co-parenting their children. Implications for practice are discussed.
GENERALIZED LINEAR AND GENERALIZED ADDITIVE MODELS IN STUDIES OF MOTORCYCLE ACCIDENT PREDICTION MODELS FOR THE NORTH-SOUTH ROAD CORRIDOR IN SURABAYA

One of the advances in the development of a model of traffic accidents is indicated by the availability of generalized linear models (GLMs) and generalized additive models (GAMs) in the regression analysis. This paper will discuss the motorcycle accident prediction models using GLMs and GAMs on the north-south road corridor in Surabaya. The first part will discuss the model prediction of traffic accidents, as well as providing a brief review related to the use GLMs and GAMs in building models of accidents. Furthermore, application examples of GLMs and GAMs will be presented. To determine the effect of non-linear in each explanatory variable, smoothing the GAMs will be conducted in each variable gradually. Model diagnostic and intrepretations will be done in the final part. The results of the application of GLMs and GAMs indicates that the development of predictive models of motorcycle accidents with statistical methods can be used to diagnose problems in road safety. GAMs produces better models than the GLMs in which its condition without using the Poisson distribution, as shown in the difference in the value of the model parameter R-sq. (Adj), deviance explained, and the GCV score. By using the Poisson distribution with a log link-function, it appears that GLMs and GAMs produce the same model parameter values.
Effect of Temperature Change and Contact-wire Wear on Current Collection Performance

For electric railways, it is preferable for the tension and height of overhead contact lines to be constant to maintain satisfactory current collection performance. Since overhead contact lines expand and contract according to temperature change, an automatic tension balancer is generally installed at the terminations. However, the tension of overhead contact lines is not always constant because of tension fluctuations in automatic tension balancers and the gradient of the yokes. In addition, contact wire wear causes its mass to decrease. This also affects the contact wire tension and height. The authors performed a theoretical study and a simulation. As the results of them, the authors clarified the effect of temperature change and contact-wire wear on current collection performance.
The relationship between perceived stress and support with blood pressure in urban Haiti: A cross-sectional analysis

Haiti is a low-income country whose population lives under repeated and chronic stress from multiple natural disasters, civil unrest, and extreme poverty. Stress has been associated with cardiovascular (CVD) risk factors including hypertension, and the impact of stress on blood pressure may be moderated by support. The distribution of stress, support, and their association with blood pressure has not been well described in low-income countries. We measured stress and support using validated instruments on cross-sectional enrollment data of a population-based cohort of 2,817 adults living in Port-au-Prince, Haiti between March 2019 and April 2021. Stress was measured using the Perceived Stress Scale, while support was measured using the Multidimensional Scale of Perceived Social Support. Continuous scores were categorized into three groups for stress (low (1–5), moderate (6–10), high (11–16), and five groups for support (low (7–21), low-moderate (22–35), moderate (36–49), moderate-high (50–64), high (65–77)). Linear regression models were used to quantify the associations between: 1) support and stress adjusting for age and sex, and 2) stress and blood pressure adjusting for age and sex. A moderation analysis was conducted to assess if support moderated the relationship between stress and blood pressure. The cohort included 59.7% females and the median age was 40 years (IQR 28–55). The majority had an income <1 US dollar per day. The median stress score was moderate (8 out of 16 points, IQR 6–10), and median support score was moderate to high (61 out of 77 points, IQR 49–71). Stress was higher with older ages (60+ years versus 18–29 years: +0.79 points, 95% CI 0.51 to 1.08) and in females (+0.85 points, 95% CI +0.65 to +1.06). Support was higher in males (+3.29 points, 95% CI 2.19 to 4.39). Support was inversely associated with stress, adjusting for age and sex (-0.04 points per one unit increase in support, 95% CI -0.04 to -0.03). Stress was not associated with systolic or diastolic blood pressure after adjustment for age and sex. Support did not moderate the association between stress and blood pressure. In this urban cohort of Haitian adults living with chronic civil instability and extreme poverty, perceived levels of stress and social support were moderate and high, respectively. Contrary to prior literature, we did not find an association between stress and blood pressure. While support was associated with lower stress, it did not moderate the relationship between stress and blood pressure. Participants reported high levels of support, which may be an underutilized resource in reducing stress, potentially impacting health behaviors and outcomes.
Network and systems support for building energy monitoring and control using wireless sensor networks

This thesis explores the application of wireless sensor network technology in residential buildings to monitor and control energy consumption. The technologies developed in this thesis provide a communications backbone so that building systems and appliances can share information. By enabling the free flow of information, we can first collect detailed building performance data. Second, we can rapidly deploy sophisticated whole-house energy optimization strategies. The centralized building automation and control systems typically found in commercial buildings are typically complex, require expert installation, and costly. In contrast, our peer-to-peer approach utilizing a self-configuring wireless network provides the flexibility and reduced cost required for the residential environment. We first explore the use of non-intrusive load monitoring to automatically extract detailed energy usage information from simple aggregate measurements. This information can, for example, be used to identify wasteful devices. To efficiently distribute sensor data throughout the wireless network, we have developed PIM-WSN, the first general-purpose multicast implementation for IP-based wireless sensor networks. To better understand how occupant behaviors impact energy consumption, we have evaluated 8 energy saving behaviors in homes for 10 weeks. The results were surprising as no single behavior significantly reduced energy consumption. However, we discovered that even motivated homeowners are unwilling to manually implement many energy saving behaviors. To explore automation of these behaviors, we developed a prototype occupancy-based solution and achieved 7%-14% energy savings in office environments. To enable deployments in residential homes, we developed a complete platform based on these demonstrated principles. From several deployments, we observed an 88% reduction in standby losses for home entertainment devices and saved 195 kWh per day by automating basic energy saving behaviors. The completed platform is highly adaptable to new problems and provides sensing and a physical interface between the building and the control algorithm. This architecture is now available as a resource to implement future control strategies in residential buildings.
Pedestrian Activity Recognition from 3D Skeleton Data using Long Short Term Memory Units

: The pace of advancement in the realm of autonomous driving is quickening, raising concerns and escalating expectations for pedestrian safety, intelligence, and stability. In dynamic and uncertain contexts, some scenarios necessitate distinguishing pedestrian position and behavior, such as crossing or standing. The ability to recognize a pedestrian is a critical component of autonomous driving success. Before making an appropriate response, the vehicle must detect the pedestrian, identify their body movements, and comprehend the significance of their actions. In this paper, a detailed description of the architecture for 3D activity recognition of a pedestrian using Recurrent Neural Networks (RNN) is presented. In this work, a custom dataset that was created from an autonomous vehicle of RRLAB at the Technische Universit¨at Kaiserslautern is employed. The information was gathered for behaviors such as parallel crossing, perpendicular crossing, texting, and phone calls, among others. On the data, models were trained, and Long-Short Term Memory (LSTM), a recurrent neural network has shown to be superior to Convolution Neural Networks (CNN) in terms of accuracy. Various investigations and analyses have revealed that two models trained independently for upper and lower body joints produced better outcomes than one trained for all joints. On a test data, it had a 97 percent accuracy for lower body activities and an 88-90 percent accuracy for upper body activities.
Whose Balance Sheet is This? Neural Networks for Banks’ Pattern Recognition

The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’ 2000-2014 monthly 25-account balance sheet data to test whether it is possible to classify them with fair accuracy. Results demonstrate that the chosen method is able to classify out-of-sample banks by learning the main features of their balance sheets, and with great accuracy. Results confirm that balance sheets are unique and representative for each bank, and that an artificial neural network is capable of recognizing a bank by its financial accounts. Further developments fostered by our findings may contribute to enhancing financial authorities’ supervision and oversight duties, especially in designing early-warning systems.
Overlap between Central and Peripheral Transcriptomes in Parkinson’s Disease but Not Alzheimer’s Disease

Most neurodegenerative disorders take decades to develop, and their early detection is challenged by confounding non-pathological ageing processes. Therefore, the discovery of genes and molecular pathways in both peripheral and brain tissues that are highly predictive of disease evolution is necessary. To find genes that influence Alzheimer’s disease (AD) and Parkinson’s disease (PD) pathogenesis, human RNA-Seq transcriptomic data from Brodmann Area 9 (BA9) of the dorsolateral prefrontal cortex (DLPFC), whole blood (WB), and peripheral blood mononuclear cells (PBMC) were analysed using a combination of differential gene expression and a random forest-based machine learning algorithm. The results suggest that there is little overlap between PD and AD, and the AD brain signature is unique mainly compared to blood-based samples. Moreover, the AD-BA9 was characterised by changes in ‘nervous system development’ with Myocyte-specific enhancer factor 2C (Mef2C), encoding a transcription factor that induces microglia activation, a prominent feature. The peripheral AD transcriptome was associated with alterations in ‘viral process’, and FYN, which has been previously shown to link amyloid-beta and tau, was the prominent feature. However, in the absence of any overlap with the central transcriptome, it is unclear whether peripheral FYN levels reflect AD severity or progression. In PD, central and peripheral signatures are characterised by anomalies in ‘exocytosis’ and specific genes related to the SNARE complex, including Vesicle-associated membrane protein 2 (VAMP2), Syntaxin 1A (STX1A), and p21-activated kinase 1 (PAK1). This is consistent with our current understanding of the physiological role of alpha-synuclein and how alpha-synuclein oligomers compromise vesicle docking and neurotransmission. Overall, the results describe distinct disease-specific pathomechanisms, both within the brain and peripherally, for the two most common neurodegenerative disorders.
The complete mitochondrial genome of Zelkova schneideriana Hand-Mazz (Ulmaceae) and phylogenetic analysis

Abstract Zelkova schneideriana Hand-Mazz is a second-class key protected wild plant in China. Here, the complete mitochondrial genome of Zelkova schneideriana Hand-Mazz was sequenced using Nanopore Sequel and Illumina NovaSeq platform. The mitochondrial genome was assembled into three circular-mapping molecules with the genome sizes of 154,640 bp, 192,388 bp and 146,907 bp, including 36 protein-coding genes, 19 tRNA genes, and 3 rRNA genes. Phylogenetic analysis indicated that Zelkova schneideriana Hand-Mazz is close with Hemiptelea davidii, a species in same Ulmaceae Mirb.
A proposal of grease pocket structure in consideration of base oil migration

The periodicity of motor disassembling maintenance is now determined by the durability of the sealed grease whose lifetime is the shortest among all bearing parts and materials, which should be lengthened further. We discussed the structure of the grease pocket and have shown that the efficiency of grease lubrication depends on the shape of grease pocket, which should be modified appropriately. We performed laboratory tests with small grease-pocket models of different shapes to find that a grease pocket structure to improve oil circulation is obtained by (1) increasing the depth of a ring-shaped grease pocket (main-GP), and (2) expanding the outer space of the grease pocket (sub-GP) upward and downward. Based on the test findings, we designed, manufactured, and tested full-size grease pocket models with a bearing rotating test machine to prove that the oil component of the grease in the sub-GP migrates in a larger quantity than when a conventional grease pocket in used. It is expected, therefore, that the proposed grease pocket structure extends the lifetime of the grease and improves the durability of bearings.
Is Organizational Citizenship Behaviour of Teachers Related to the Effectiveness of Higher Education Institutions?

This research investigates the relationship between organizational citizenship behaviour (OCB) of university teachers and organizational effectiveness (OE) of Indian Universities. This cross-sectional survey includes 721 teachers from various state and private universities in Rajasthan, India. The findings reveal a positive and significant relationship between organizational citizenship behaviour and organizational effectiveness. Additionally, the state universities show a higher variance of 78% in organizational effectiveness by organizational citizenship behaviour as compared to private universities with 49% variance. This research establishes the conceptual and empirical linkage between OCB and OE within higher education institutions in the Indian context. This study provides some practical suggestions for university administrators for effective institutional management. It also offers input to policymakers to consider OCB as a new and innovative element of appraisal dimension and compensation system apart from traditional parameters of HR systems.
Generating Unified Platforms Using Multigranularity Domain DSE (MG-DmDSE) Exploiting Application Similarities

Heterogeneous accelerator-rich (ACC-rich) platforms combining general-purpose cores and specialized HW accelerators (ACCs) promise high-performance and low-power streaming application deployments in a variety of domains, such as video analytics and software-defined radio. In order to benefit a domain of applications, a domain platform exploration tool must take advantage of structural and functional similarities across applications by allocating a common set of ACCs. A previous approach proposed a genetic domain exploration tool (GIDE) that applied a restrictive binding algorithm that mapped applications functions to monolithic accelerators. This approach suffered from a low average application throughput across and reduced platform generality. This article introduces a multigranularity-based domain design space exploration tool (MG-DmDSE) to improve both average application throughput as well as platform generality. The key contributions of MG-DmDSE are: 1) applying a multigranular decomposition of coarse-grained application functions into more granular compute kernels; 2) examining compute similarity between functions in order to provide more generic functions; 3) configuring monolithic ACCs by selectively bypassing compute elements within them during DSE to expose more functionality; and 4) speeding up MG-DmDSE platform allocation exploration through a greedy guided mutation (GGM) algorithm. To assess MG-DmDSE, both GIDE and MG-DmDSE were applied to applications in the OpenVX library. MG-DmDSE achieves an average $2.84\times $ greater application throughput compared to GIDE. Additionally, 87.5% of applications benefited from running on the platform produced by MG-DmDSE versus 50% from GIDE, which indicated increased platform generality. The generated MG-DmDSE platforms achieve an average of 61.8% logarithmic throughput improvement for unknown applications over GIDE. GGM results in saving 84.8% of the exploration time in MG-DmDSE with only 0.23% performance loss.
Free Material Optimization and the Plato-N software

Recently, a new class of multidisciplinary free material optimization (MDFMO) models has been introduced. In the framework of the EU FP6 project Plato-N, efficient algorithms for the solution of MDFMO problems have been developed and implemented in the software platform Plato-N. The software platform allows for definition and solution of MDFMO problems as well as visualisation and interpretation of the optimal results.
The Protective Effect of Moringa Oleifera Seeds Extract on Liver Damage in Mice

The current study to assess the antioxidant and hepatoprotective properties of Moringa oleifera seed extract versus carbon tetrachloride (CCl4)-stimulated liver damage. Materials &Methods: Male albino mice were separated into four groups for the investigation The control group (A) was given distilled water (DW), whereas the other three groups (B, C, and D) were administered CCl4 intraperitoneally (IP) (0.5 ml/kg) mixed with corn oil (vol/vol), for 8 weeks, twice a week. Group (C) was given Moringa seed extract (1g/kg) orally every day for 8 weeks, while group (D) was given corn oil orally, and the groups (C) and (D) were compared to group (B) which was given CCl4 IP. The researchers measured liver functions, blood-reduced glutathione (GSH), total antioxidant capacity (TAC), and malondialdehyde (MDA) in erythrocytes. Moreover, catalase enzymes in liver tissue homogenate were also assayed and superoxide dismutase (SOD), in addition to liver histopathology. Results : Following a CCl4 injection, liver enzymes and MDA levels were elevated. Conversely, TAC, serum albumin, total protein, catalase, tissue SOD, and blood glutathione levels were all decreased. Administration to Moringa oleifera seeds extract reduces MDA levels and enzyme activity in the liver while increasing antioxidant activity. Simultaneously, after treatments with this extract, the histological damage effects of CCl4 intoxication were also improved. Conclusion: Finally, Moringa oleifera seeds extract increased innate antioxidant activity and reduced CCl4-induced liver damage, suggesting that it might be employed as a hepatoprotective medication in the future.
Development of data mining-based big data analysis methodologies for building energy management

of the thesis entitled: Development of Data Mining-Based Big Data Analysis Methodologies for Building Energy Management Submitted by : Fan Cheng For the degree of : Doctor of Philosophy at The Hong Kong Polytechnic University in Feb 2016 Today’s buildings are becoming not only energy intensive, but also information intensive. Building Automation Systems (BASs) are widely installed in modern buildings for automatic monitoring and control of the operation of various building services systems. BASs collect and store a huge number of sensor measurements and control signals at short time intervals. The effective utilization of the big BAS data can help to optimize and diagnose the performance of buildings so as to improve their operational performance. However, the big BAS data are not fully utilized due to the lack of advanced data analysis techniques and tools. BASs can only perform simple data analysis, such as historical data tracking, moving averages and benchmarking. Data mining (DM) is a promising solution for the knowledge discovery from massive data