Miracle-dz/UniFAR-FTUs huggingface dataset:
Hard Thresholded Regression Via Linear Programming

This aim of this paper is to develop a hard thresholded regression (HTR) framework for simultaneous variable selection and unbiased estimation in high dimensional linear regression. This new framework is motivated by its close connection with the L 0 regularization and best subset selection under an orthonormal design, while enjoying several key computational and theoretical advantages over many existing penalization methods (e.g., SCAD or LASSO). Computationally, HTR is a fast two-stage estimation procedure consisting of the ﬁrst step of calculating a coarse initial estimator and the second step of solving a linear program problem. Theoretically, under some mild conditions, the HTR estimator is shown to enjoy the strong oracle property and thresholded property even when the number of covariates grows at an exponential rate. We also propose to incorporate the regularized covariance estimator into the estimation procedure in order to have a better trade oﬀ between noise accumulation and correlation modeling. Under this framework, HTR includes Sure Independence Screening as a special case. Both simulation and real data results show that HTR outperforms other state-of-the-art methods.
School Board of Management’s Financial Resource Practices vis-a-vis Student Performance: A Case of Two Counties in Kenya

School Board of Management’s (BOM) practices have been found to have a tremendous effect on students’ academic performance. However, BOM’s quality of management has been questioned by many studies in Kenya and other countries. This paper is based on a research carried out to assess the extent to which BOM’s financial resource management practices influence students’ performance at Kenya Certificate of Secondary Education (KCSE) in Kitui and Makueni counties. The study dealt with the following 10 financial practices which have been found to influence performance in secondary schools: aligning the budgets to school vision, approval of the budget, approval of schools expenditure, supervision of books of accounts and trial balances, deliberation of audited reports, monitoring of school expenditure, fundraising activities, soliciting for grants donations and bursaries, accountability in school finances, and ensuring projects are in line with school’s strategic plans and how they related to student’s performance. The study used mixed method approach with both qualitative and quantitative data analysis. Correlational research design was used. The target population was 4,270 BOM members from 305 public secondary schools in Kitui County and 4,536 from 324 in Makueni County. Questionnaires were used to collect data from BOM members whereas an interview schedule got data from the school principals. Overall, and to a great extent, the findings show that BOM’s financial resource practices, in both counties, influenced students’ performance. Since majority of the BOM members indicated that they lacked adequate competencies, the study recommends continuous induction and training on the following areas: initiating and carrying out projects that are in line with the strategic plans of the schools; supervision of books of accounts; soliciting for grants, donations and bursaries; budgeting; organizing for fundraising; and monitoring of school expenditure.
Overview of Naming Entities Based on Natural Language Processing

: Named entity recognition is an important research direction of natural language processing. This paper first reviews the development process and main stages of named entity recognition, then expounds the research contents and methods of named entity recognition, and puts forward the key and difficult points of named entity recognition, The development process of named entity recognition is from the initial rule-based and dictionary based method to the later statistical learning method, and then to the mixed learning method and some popular learning methods. Finally, the development of named entity recognition is prospected.
Impact of propolis as a bio-product on pepper plants productivity and their fruits at shelf-life period

Two experiments were conducted to investigate the influence of propolis ethanolic extract (PEE) on development and productivity of sweet pepper plants in the first experiment and both of PEE and propolis aqueous extract (PAE) through fruit shelf life at room temperature during June of seasons 2019 and 2020 for 7 days in the second experiment. The concentrations of the two extracts were 3%, 6%, 9% and 12%. The results indicated that PEE at concentrate 9% was sufficient to obtain maximum values of vegetative growth yield characters i.e., fruit diameter, average fruit weight and total yield/feddan except plant dry weight which registered highly values with level 6%. The augment results for chemical fruit content i.e., TSS and ascorbic acid were obtained by the application of 12% of PEE. Data also indicated in the second experiment after shelf life period that PEE and PAE at concentrate 12% was significantly able to obtain the least percentage of fruit physical characters expressed as fruit weight, length and diameter especially PEE which was most superior to PAE. Concerning fruit chemical contents, dipping the fruits in the high concentrate 12% of PEE was the most effective in reducing the increase of TSS, while the same concentrate of PAE had the ability to reduce loss of ascorbic acid. On the other hand, both of two extracts PEE and PAE at high level 12% resulted in reduced fruit deterioration percentage in treated fruits especially those produced from plants that were treated by the same level (12%) in comparison with control treatment.
Round Robin Test on Radiated Noise of a Cavitating Propeller

Mitigation of shipping noise is a topical issue in marine engineering because of the dramatic increase in the levels of anthropogenic underwater noise and its impact on marine life. In recent years, hydro-acoustic research has been very much focused on the development of reliable methods for predicting underwater radiated noise (URN) due to cavitation, which is known to be the dominant contribution to the overall radiated noise spectrum of ships above the cavitation inception threshold. Model-scale measurements are currently considered the most reliable approach to study URN problems in naval and marine engineering and are crucial for the verification and validation of numerical methods. However, their reliability is affected by several uncertainty sources for which suitable test procedures and post-processing techniques are needed. As a means to better understand the accuracy and reliability of underwater radiated noise measurements, a round robin (RR) test programme for an open water propeller setup was organized within the Community-of-Practice “Noise” of the HydroTesting Forum, with the aim of comparing results among several institutes (i.e. University of Genova UNIGE, University of Strathclyde, NMRI, SSPA, KRISO, CNR-INM and MARIN). This paper reports an overview of the RR programme and compares the different approaches and results.
Research on Racial Discrimination in Indonesia and Ethnic Groups

Discrimination on the basis of race is widely condemned, but on the basis of nationality it is widely accepted. Is it not odd that you may not discriminate against me for where my great-great-grandparents were born, but you may discriminate against me for where my parents were born. This article learns about racial discrimination in Indonesia and ethnic groups by studying the discrimination of TOTOK and PERANAKAN.
Arbeitskreis Quantitative Steuerlehre Quantitative Research in Taxation – Discussion Papers

This study examines firms’ voluntary disclosure of tax loss carryforward (TLCF) information. We identify a setting (conservative German accounting environment) under which the demand for TLCF disclosure is particularly pronounced and analyze firms’ TLCF disclosure behavior while controlling for overall reporting quality. We argue and find that uncertainty about the usability of TLCF is the main driver of voluntary TLCF disclosure. Value relevance tests suggest that the incremental effect of disclosure under uncertainty is positive, indicating the usefulness of firms’ voluntary disclosure. We also find that the content and type of disclosure vary systematically with the signal of uncertainty. While firms provide different kinds of complex TLCF information, investors only value very basic and less complex information. Our study provides detailed and unique insights into TLCF disclosure, documenting that managers enrich the information environment with voluntary disclosure that caters to expected investors’ needs, but the perception of the content’s usefulness is limited to simple information.
Chromosomes Identi�cation based Differential Evolution (CIDE): A New Bio-inspired Variant for Network Intrusion Detection

Chromosomes identi�cation is of the paramount importance. The centromeric location of chromosomes is used to classify different species. Plethora of research has already been done in the domain of Bio-inspired computation. However, a Bio-inspired variant of Differential Evolution (DE) is still not designed. In this work attempt has been made for this pursuit and a new variant named as “Chromosomes Identi�cation based Differential Evolution (CIDE)” has been proposed. This technique is used to extract the knowledge from the databases and it constructs effective and reliable decision rules for the purpose of classi�cation. Further, the testing accuracies of proposed CIDE technique has been compared with nearest neighbour classi�er (1-NN) optimized by various existing variants of differential evolution and 1-R classi�er using six-real datasets, mainly from medical �eld. The experimental analysis reveals the fact that proposed technique is superior to 1-NN and 1-R classi�er. Over and above, Friedman test has also been conducted to validate the superiority of proposed CIDE technique. Moreover, the proposed CIDE algorithm is also tested on KDDCup’99 dataset and different parameters such as classi�cation accuracy, false alarm rate and precision are computed.
Standardization of optimum conditions for hydrolyses of goat milk whey protein with alcalase enzyme

The study was conducted with an objective to develop a goat milk whey protein hydrolysate (GMWPH) with enhanced antioxidant property and better Ca chelating activity. Goat milk whey protein was digested with commercial food-grade alcalase enzyme under various conditions of incubation temperature (30 to 70 C), incubation time (30 to 300 min), enzyme concentration level (0.25 to 2%) and pH (6 to 10) of the enzyme reaction to achieve the best hydrolysis. The hydrolysates were analyzed for degree of hydrolysis (DH), antioxidant activity (ABTS) and calcium chelating capacity. It was found that treatment with alcalase at 60 C incubation temperature, 60 min incubation time, 0.5% enzyme concentration and 7.0 pH effectively degraded the goat milk whey proteins, as determined by SDS-PAGE and measurement of nonprotein nitrogen content. Hydrolysis with alcalase resulted in a significant increase in antioxidant and Ca chelation property. Hence, the GMWPH may be useful for development of novel functional foods for infants, and the elderly osteoporosis patients to replace cow milk.
The Lead-Lag Relationship between VIX Futures and SPX Futures

We study the lead-lag relationship between VIX futures and SPX futures on a sample of high-frequency data using the cross-correlation function. The analysis reveals large timevariation in the lead-lag relation. We find that cross-market activity explains a major part of the lead-lag relation and that days of high activity are associated with a strengthened VIX futures lead over SPX futures. As VIX futures hedging by dealers generate cross-market activity, this indicates that the strengthening of the VIX futures leadership could be partly explained by dealers’ hedging activities. We also find evidence that the hedging channel can move the SPX futures market for reasons unrelated to price discovery.
Internal Planetary Feedbacks, Mantle Dynamics, and Plate Tectonics

Isolating planetary feedbacks, and feedback analysis, are prevalent aspects of climate and Earth surface process science. An under appreciation of internal planet feedbacks, and feedback analysis for plate tectonics research, motivate this chapter. We review feedbacks that influence the Earth’s thermal evolution and expand them to include magmatic history and planetary water budgets. The predictions from feedback models are shown to be consistent with petrological constraints on the Earth’s cooling. From there, we isolate feedbacks that connect structural elements within the mantle dynamics and plate tectonics system. The feedbacks allow for a reciprocal causality between plates, plumes, the asthenosphere, and mantle flow patterns, with each element being co-dependent on the others. The linked elements and feedbacks define plate tectonics are part of a self-sustaining flow system that can bootstrap itself into existence. Within that framework, plate tectonics involves the co-arising of critical system factors. No single factor is the cause of another. Rather, they emerge with the links between them and the generation of functional elements coincides, within relatively narrow time windows, with the co-emergence of factors that are critical for the maintenance of the elements themselves. What emerges is not a tectonic state but a process. That is, a set of feedbacks that can transform the tectonics of a planet and/or maintain plate tectonics. The feedback functions are not permanent but can operate over extended time frames such that plate tectonics can remain stable. The nature of the feedbacks, and their stability, can be studied at various levels of detail but questions of origin can become ill-defined. Observational tests of a feedback framework for plate tectonics and mantle dynamics are presented, along with research paths that apply feedback methodology to solid planet dynamics and comparative planetology. Preprint submitted to TBD March 18, 2022
Cumulative Metabolic and Epigenetic Effects of Paternal and/or Maternal Supplementation with Arachidonic Acid across Three Consecutive Generations in Mice

Apart from the known associations between arachidonic acid (AA), weight gain, and neurological and immune function, AA exposure leads to alterations in global and gene-specific DNA methylation (DNAm) and fatty acid (FA) content in human cultured cells. However, it is unknown as to whether the latter effects occur in vivo and are maintained over extended periods of time and across generations. To address this issue, we asked whether AA supplementation for three consecutive generations (prior to coitus in sires or in utero in dams) affected offspring growth phenotypes, in addition to liver DNAm and FA profiles in mice. Twelve-week-old BALB/c mice were exposed daily to AA dissolved in soybean oil (vehicle, VH), or VH only, for 10 days prior to mating or during the entire pregnancy (20 days). On average, 15 mice were supplemented per generation, followed by analysis of offspring body weight and liver traits (x average = 36 and 10 per generation, respectively). Body weight cumulatively increased in F2 and F3 offspring generations and positively correlated with milligrams of paternal or maternal offspring AA exposure. A concomitant increase in liver weight was observed. Notably, akin to AA-challenged cultured cells, global DNAm and cis-7-hexadecenoic acid (16:1n-9), an anti-inflammatory FA that is dependent on stearoyl-CoA desaturase 1 (SCD1) activity, increased with milligrams of AA exposure. In accordance, liver Scd1 promoter methylation decreased with milligrams of germline AA exposure and was negatively correlated with liver weight. Our results show that mice retain cellular memories of AA exposure across generations that could potentially be beneficial to the innate immune system.
Biological profile of secondary hyperparathyroidism in chronic renal failure

Introduction: Secondary hyperparathyroidism (SHPT) is a frequent complication of chronic renal failure and is associated with high morbidity and mortality. Our goal is to study the average time of development of SHPT, and the biological profile in accordance with recommended standards. Patients and methods: Retrospective descriptive study, including 134 chronically hemo-dialysed patients. Demographic, clinical, phosphocalcic and therapeutic data were analyzed. The phosphocalcic parameters were defined by KDOQI 2003 and KDIGO 2009 recommendations. Results: The average time to onset of SHPT is 3.15 ± 4.02 years according to KDOQI standards and 4.6 ± 4.3 years according to KDIGO standards. The most commonly administered SHPT treatments are calcium salt and Vitamin D active. At the end of the study, 73.9% for KDOQI and 46.3% for KDIGO had hyperparathyroidism. The rates of compliance of the phosphocalcic indicators were higher with the KDIGO than with KDOQI recommendations for serum calcium, phosphate and parathyroid hormone (PTH). Discussion: We noted a significant improvement between the two initial and recent periods concerning phosphocalcic parameters in general and hyperparathyroidism in particular. We found that the delay in the diagnosis of CKD, the duration of dialysis, the cost and availability of treatments, and the minimal frequency of kidney transplant in our country make it difficult to get the biological parameters to the recommended targets, Conclusion: It is necessary to underline the interest of a good biological monitoring to ensure a good evaluation of the therapeutic conduct, and a better prevention against the serious complications of SHPT.
Convolutional neural network for real-time main transformer detection

For substation constructions, the main transformer is the dominant electrical equipment, and its arrival and operation affect the progress of project directly. In the context of smart grid construction, in order to improve the efficiency of real-time main transformer detection, this paper proposes an identification and detection method based on the SSD algorithm. The SSD algorithm is able to extract the target device (such as main transformer) accurately and the Lenet algorithm module can analyse the features contained in the image. To improve the accuracy of the detection method, the image migration algorithm of VGG-Net is used to expand the negative samples of main transformers to improve the generalisation of the algorithm. Finally, the image set collected in the real substation projects is used for validation, and result shows that the method identifies main transformers more accurately, with high effectiveness and feasibility.
Community Public Open Space Planning Based on Green Infrastructure with the Priority of Biodiversity

Maintaining and restoring urban biodiversity is one of the basic functions of green infrastructure. Starting from the ecological problems of community public open space and the insufficiency of traditional planning methods, the biodiversity of green infrastructure attempts to be integrated into public open space planning in community-scale. After discussing the research on the combination of community public open space and green infrastructure based on biodiversity, ‘the interconnections between green infrastructures’ is the key to achieving both benefits at the same time. Finally, taking Nanjing Xinjiekou Street in China as an example, the renewal planning method of community public open space on the priority of biodiversity is elaborated and corresponding suggestions are put forward. In a word, the reasonable planning and layout of community green infrastructure based on biodiversity can make up for the disadvantages of traditional planning and renewal methods of public open space to a certain extent, solve the ecological and social problems of public open space, and realize the symbiosis of people and nature in community.
The efficiency of sewage treatment plant: A case study at the main campus of Universiti Malaysia Sabah (UMS)

The aquatic nature, including streams, rivers, marine, and groundwater, becomes susceptible to most contaminants’ effects through various pathways known as point and non-point sources. Point source pollution from inefficient sewage treatment plants (STP) will likely produce effluents that could deteriorate the environment. Therefore, this study assesses the efficiency of STP at Universiti Malaysia Sabah (UMS) based on the Malaysian Sewerage Industry Guidelines (MSIG). The design parameters of the STP and its influent and effluent physicochemical parameters are evaluated and compared between usage peak time (lecture-week) and non-peak (semester-break) conditions. The measurement was based on the Standard Methods of Examination of Water and Wastewater. The permissible limits of influent and effluent from Malaysia Sewerage Industry Guidelines (MSIG) are referred. In contrast, the efficiency of the STP is calculated as the percentage reduction of pollutants. The findings revealed that the current STP’s design parameters complied with the MSIG standard. The suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), oil and grease (O&G) at influent during peak time is typically higher than during the non-peak time, albeit a timely sudden increase can be observed. The effluent quality is inconsistent but is still within permissible limits, except for O&G, which occasionally exceeds the allowable value. As for the BOD, a consistent 80% removal efficiency is observed. Hence the effluent reuse for agricultural purposed and landscape irrigation is proposed due to the environmental quality potential and reduction to direct released toward the receiving water streams.
Mine track obstacle detection method based on information fusion

As an important part of the coal mine transportation system, coal mine underground rail transportation undertakes the core transportation task of coal mine underground. Its safe and efficient operation is directly related to the efficiency of coal mine production and transportation. In view of this, this paper proposes a mine track environmental obstacle detection system which integrates camera and Lidar information to realize real-time automatic detection of obstacles in front of the underground track mine cart. The specific research results are as follows: first of all, a point cloud clustering algorithm for the mine environment is designed to extract the obstacle information, and then the YOLOv5 algorithm is used to identify the obstacle information in the image. Finally obstacle information from image and point cloud are fused at the decision-level. The obstacle detection method proposed in this paper can be successfully identified to meet the requirements of the obstacle object detection function of the rail vehicle.
The perception of rural dairy farmers concerning the commercialization of forage: Evidence from Lembang District

Environmental and land pressure from horticultural and tourism reduces the availability of forage for sustainable dairy production, specifically in Lembang. Subsequently, only limited commercialization of forage exists in the area. This study aims to explore the perception and selections of dairy farmers in Lembang District based on their interest in purchasing forage and its varieties. The purposive sampling technique was used to select hundreds farmers and they were interviewed for their perception. These perceptions were grouped into (1) not very interested, (2) not interested, (3) interested and (4) very interested. The data were analyzed using scale range analysis. However, the ones concerning the selection of forage types and the willingness of farmers to purchase this plant material were analyzed using qualitative analysis and descriptive statistics. The results showed that 96% were willing to purchase forage (interested and very interested) with elephant grass being the most favourable type (62%). It was found that quality is the critical determinant in farmers decision to purchase forage (33%). Therefore, it was concluded that sustainable dairy farming in the district can be improved by purchasing high-quality elephant grass forage from outside this region.