Miracle-dz/UniFAR-FTUs huggingface dataset:
Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization

Malaria disease can indeed be fatal if not identified and treated promptly. Due to advancements in the malaria diagnostic process, microscopy techniques are employed for blood cell analysis. Unfortunately, the diagnostic process of malaria via microscopy depends on microscopic skills. To overcome such issues, machine/deep learning algorithms can be proposed for more accurate and efficient detection of malaria. Therefore, a method is proposed for classifying malaria parasites that consist of three phases. The bilateral filter is applied to enhance image quality. After that shape-based and deep features are extracted. In shape-based pyramid histograms of oriented gradients (PHOG) features are derived with the dimension of N × 300. Deep features are derived from the residual network (ResNet)-50, and ResNet-18 at fully connected layers having the dimension of N × 1,000 respectively. The features obtained are fused serially, resulting in a dimensionality of N × 2,300. From this set, N × 498 features are chosen using the generalized normal distribution optimization (GNDO) method. The proposed method is accessed on a microscopic malarial parasite imaging dataset providing 99% classification accuracy which is better than as compared to recently published work.
Characterization of Levan Fructan Produced by a Gluconobacter japonicus Strain Isolated from a Sugarcane Processing Facility

During raw sugarcane processing, a significant portion of lost sucrose is attributable to microbial degradation. Sucrose consumption by many bacteria is also linked to the production of exopolysaccharides (EPS) such as dextrans and fructans. These resulting EPS cause operational challenges during raw sugar manufacturing. Here, we report the characterization of EPS from a fructan-forming Gluconobacter japonicus bacterium that we previously isolated from a Louisiana sugarcane factory. The genome sequencing revealed the presence of two encoded levansucrase genes, lsrA and lsrB. One levansucrase, LsrB, was detected in the secreted protein fraction of G. japonicus LASM12 by QTOF LC-MS. The spotting assays indicated that G. japonicus produces EPS using sucrose and raffinose as substrates. The G. japonicus EPS correlated with levan fructan commercial standards by 1H-NMR, and with the characteristic carbohydrate fingerprint region for FTIR spectra, confirming that the G. japonicus EPS is levan fructan. The glycosyl composition and glycosyl linkage analysis revealed a linear β-2,6-fructofuranosyl polysaccharide with occasional (5.7%) β-2,1-fructofuranosyl branches. The gel permeation chromatography of the levan fructan EPS showed two main peaks at 4.5 kDa and 8 kDa and a very minor peak at 500 kDa. G. japonicus was identified as a producer of levan fructan. These findings will be useful for future studies aimed at evaluating the impact of levan fructans on sugar crop processing, which have been historically underestimated in industry.
Replication Data for: What influences transfer of training in an African agricultural research network?

What influences transfer of training in an African agricultural research network? The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications. Citation: Muthoni Andriatsitohaina, Rachel; Miiro, Richard Fred. 2016. What influences transfer of training in an African agricultural research network? The Journal of Agricultural Education and Extension 1-15 p.
Trophic Abilities of Trametes Gibbosa (Polyporales, Basidiomycota) With Respect to The Wood of Fagus Sylvatica

Trametes gibbosa is a widespread polypore fungus occurring throughout Europe and Asia. It is considered a saprotroph occasionally exhibiting parasitic activity. The aims of the present experimental work were to: (1) determine the ability of T. gibbosa pure culture to decompose beech wood under laboratory conditions, (2) test the trophic abilities of T. gibbosa by comparative analysis of the development of its mycelium inoculated into the stems of living beech trees and into beech rollers (stem segments – dead substrate). The laboratory wood decay experiment revealed considerable weight loss of the beech wood samples exposed to T. gibbosa (32.7% after 120 days). Identification of the mycelium used for inoculation in natural forest conditions was done by morphological analysis of the cultures and molecular barcoding. Attempts to recover T. gibbosa mycelium were made after two and eight years for the living trees, and after two years for the rollers. While T. gibbosa could not be reisolated from the inoculated beech trees, basidiomata were observed on the beech rollers, and genetically verified T. gibbosa mycelium was extracted from them. We conclude that T. gibbosa exhibits strong saprotrophic inclinations.
Epizootological monitoring studies on the spread of nematodirosis of cattle in the territory of Poltava region

The most common helminthiasis of cattle is strongylidosis of the digestive organs. This group of animal helminthiasis is widespread in Ukraine and is caused by nematodes of the Strongylida suborder, among which nematodirosis occupies a leading place. Invasion leads to a decrease in productivity, the quality of the obtained products, barrenness of the broodstock, and deterioration of the growth and development of the young. Invasion of definitive hosts occurs mostly passively; by alimentary means, the invasive is the larva of the third stage. The work aimed to conduct monitoring studies of the epizootic situation regarding the spread of nematodirosis of cattle in the territory of Poltava region based on the results of the analysis of statistical data of the reporting documentation of the Main Department of the State Production and Consumer Service in Poltava region for 2018–2022. It was established that the extent of nematodirosis infestation in cattle ranged from 0.81 % (in 2020) to 5.30 % (2022), with the average extensiveness of the invasion for the studied period at 1.36 %. At the same time, the share of nematodirosis among other nematode diseases of cattle was 18.37 %. Among other nematodes, neoascarosis, bunostomosis, esophagostomosis, dictyokaulosis, and trichurosis were diagnosed in the territory of the studied region, the share of which ranged from 1.51 to 51.51 %. Among the invasive diseases of cattle registered on the territory of the Poltava region, the share of trematodes (fasciolosis, dicroceliosis, paramphistomatosis) in the general parasitic pathology is 85.51 %, protozoans (eimeriosis, babesiosis) and nematodes – 8.67 and 5.78 %, respectively, for cestodoses (moniasis) – 0.04 %. The obtained data from monitoring studies indicate the relevance of a more in-depth study of the spread of nematodirosis infestation among cattle in farms of the Poltava region, taking into account the nematode fauna, the characteristics of the infestation of animals of different age groups, in different seasons of the year, and as part of mixed infestations with other parasites.
A New Class of Bessel Kernel Functions for Support Vector Machine

In this paper, we construct a Bessel-class kernels for Support Vector Machine. This new class of kernels are proved that they are continuous and satisfy Mercer’s condition. The presented Bessel-class kernels can degenerate to Gaussian kernel in an infinite smooth case. Compared to the other kernels, these present Bessel kernels can be flexibly applied to classification and regression with fewer constants to be adjusted. Additionally, four simulation experiments including classifications and regressions, have been carried out to show the good performance of these Bessel-class kernels.
A new specimen of Agorophius pygmaeus (Agorophiidae, Odontoceti, Cetacea) from the early Oligocene Ashley Formation of South Carolina, USA

Abstract. The holotype partial skull of Agorophius pygmaeus (the monotypic form for both the genus Agorophius and the Family Agorophiidae) has been missing for approximately 140 years. Since the discovery of Agorophius pygmaeus, many additional taxa and specimens have been placed in the Family Agorophiidae, only to be reclassified and removed later. This has created confusion as to what is and what is not an agorophiid and a lack of clarity as to what characteristics delimit the Agorophiidae. A newly discovered skull of an agorophiid recently collected from an underwater cliff face of the Ashley River, South Carolina, USA, is assigned to Agorophius pygmaeus. It derives from the base of the Ashley Formation (early Oligocene). The new specimen consists of most of the skull and periotics, which are well preserved and described for the first time in an agorophiid. The new specimen provides an opportunity to diagnose the Agorophiidae and place the genus and species within the phylogenetic context of the early odontocete radiation in the Oligocene, along with other taxa such as the Ashleycetidae, Mirocetidae, Patriocetidae, Simocetidae, Waipatiidae, and Xenorophidae. Based on this new understanding, Agorophiidae are known with certainty only from the early Oligocene of South Carolina, with other undescribed, potential agorophiid specimens from the Oligocene of the North Pacific region (Japan, Mexico, and Washington State).
Next-Generation Engineering: What are the Types of Real-World Problems that Our Students Want to Solve?

This research category full paper aims to contribute to the body of research in the motivation of engineering students to engage in sociotechnical problems. Students' characteristics are in constant evolution and the core of those currently enrolled in college belong to Generation Z, who have been documented to be more identified with sustainability goals, and with higher civic engagement than previous generations. In addition, research has shown that students with marginalized identities are more interested in engaging in engineering to contribute to solving complex social problems. While engineering has significant potential to contribute to sustainability, engineering education has often failed to strengthen such orientations for students. To explore the capital of students motivations at their time of entry to an engineering program we engage in a qualitative study of student artifacts generated within a first-year engineering seminar in Fall 2022. This study aims to gauge their interest when they start their engineering degrees and explore the differences in such interest by their intended engineering major and demographic characteristics. We used the reflections of 481 first-year undergraduate engineering students to explore which aspects of care guided the type of problems they were interested in tackling in the world. Framed under Ehrenfeld's taxonomy of concerns for sustainability we found that many students were motivated by one or more forms of care, care for the world, care for others, care for self. There was some overlap between the considered caring spaces; however, 21% of the students in our study were motivated by purely technical issues, demonstrating the need for strengthened connections between the teaching of engineering design and the ways that such a design may impact or create meaning in society The identification of problems that interest and motivate undergraduate engineers brings valuable insights into the preparation of more thoughtful and effective engineers.
Analysis of Vlog Transcripts using Topic Modeling, Summarizer, and Cluster Analysis

This paper demonstrates the use of natural language processing (NLP) models to analyze qualitative data for engineering education research purposes. Three NLP models are applied: topic modelling, which identifies salient keywords in text; summarizer, which extracts and concatenates sentences with unique meaning from a text; and cluster analysis, which groups texts together based on similar word sequences. The study applied these techniques to short video logs, or vlogs, collected as part of a case study of undergraduate engineering students' exposure to a social innovation curriculum. The curriculum aimed to teach students to use their engineering skills to identify and address issues that cause suffering for marginalized communities. In the vlogs, students responded to prompts asking them to describe what social issues they are passionate about, how they relate to these issues, and how they would propose exploring and addressing those issues. Vlogs were transcribed, pre-processed, and then examined with each technique to identify patterns within and across the prompts. Widely-recognized limitations of NLP techniques included the potential loss of important contextual information and the dependence on large volumes of data to produce valid and reliable results. Despite these limitations, the combination of three techniques was effective for locating high priority transcripts within the data corpus, identifying themes within and across vlogs, and supporting longitudinal analysis of student responses. Previous literature has documented the utility of topic modeling and other NLP techniques to analyze large volumes of written text on, for example, course evaluations or student writing assignments. Importantly, this study demonstrates the novel and meaningful application of topic modelling, summarizer, and cluster analysis to analyze a relatively small corpus of transcript data. Given these results, we are optimistic about the potential for NLP approaches to complement other analysis techniques and make the analysis of transcript data more efficient and feasible.
Development Of A Portable Incubator For The Detection Of Coliform In Water Using Iot

Ensuring clean and safe drinking water is a fundamental human right, yet many lack access, leading to waterborne diseases that claim lives globally. This project aims to create an affordable, portable coliform bacteria detection incubator. It employs a light-dependent resistor (LDR) and an IoT-enabled microcontroller (ESP32) to maintain ideal conditions for incubation. The system uses a Digital Humidity and Temperature (DHT11) sensor and heating element to sustain a 35-37°C temperature range, optimal for coliform bacteria growth. An integrated Liquid Crystal Display (LCD) records resistance and temperature data. Collected information is transferred to the microcontroller and streamed to the Blynk app for real-time monitoring. This innovation's portability and cost-effectiveness make it suitable for remote regions with limited access to laboratory equipment. Significantly, it holds promise for advancing low-cost, portable water quality monitoring systems, potentially enhancing public health.
NM 14. DETERMINACIÓN DEL EFECTO DE INCLUSIÓN DE 3 NIVELES DE FOLLAJE DE BATATA (Ipomoea batatas (L.) Lam.) SOBRE LA VELOCIDAD DE TRÁNSITO HASTA EL ILEÓN

Determination the effect of the addition of sweet potato foliage on the rate of food passage to ileum In order to determine the effect of the addition of sweet potato foliage ( Ipomoea batatas (L.) Lam. ) on the rate of passage an assay using four 35 kg castrated male hybrid pigs with an ileo-rectal anastomosis was performed: a 4 x 4 Latin square treatment design was used. The treatments were: Basic diet (BD) , BD+10 % foliage (F), BD + 20 %F and BD + 30 %F Each period lasted five days, three of which were used to adapt to the diet and 2 days were used for ileal digesta sampling On the first sampling day, 6 mg of chromic oxide was administered. The ileal digesta was collected two hours during the day and three hours during the night. Accumulated marker recovery values led to the equation: Logrec Cr = - 0.245 + 1.330 loghour + 0.015 foliage level 72.41 % R 2 adjusted to the application of the equation it was determined that with increase in the amount of foliage the marker appears faster(TA 5 %)(P < .05) and the time it take to disappear is less which means less retention(P<.05)
High-throughput screening identification of novel immunomodulatory combinations for the generation of tolerogenic dendritic cells

Introduction Tolerogenic Dendritic Cells (tolDCs) have an exceptional promise as a potential therapy for autoimmune disease and transplantation rejection. TolDCs are a unique phenotype of antigen presenting cells (APCs) that can influence naïve T cells into antigen specific T regulatory cells (Tregs), which can re-establish tolerance against auto/allo-antigens in the long term. Despite their promise, tolDCs have not found clinical success. Most strategies seek to generate tolDCs ex vivo by differentiating naïve dendritic cells (DCs) with immunosuppressive agents. Recently, we developed a tolDC generation strategy, which we call Push/Pull Immunomodulation (PPI). In PPI, DCs are treated with combinations of toll-like-receptor (TLR) agonists and immunomodulatory agents, which generate more robust, Treg-inducing tolDCs than previous strategies. Here, we seek to identify more potent and clinically viable PPI formulations using data from a high-throughput screening project. Methods Over 40,000 combinations of pathogen-associated molecular patterns (PAMPs) and immunomodulatory small molecules were screened using a modified murine macrophage line, RAW dual cells, to observe the effect of these combinations on two major immune regulatory transcription factors, NF-κB and IRF. Combinations were further screened for inflammatory cytokine activity using a human monocyte cell line, THP-1, then on murine DCs. Leading candidates were co-cultured with T cells to assess antigen specific T cell responses. Results From this data, we identified 355 combinations that showed low or moderate IRF activity, low NF-κB activity, low inflammatory cytokine generation and good viability: all hallmarks of tolerogenic potential. We further screened these 355 combinations using bone marrow derived DCs (BMDCs) and identified 10 combinations that demonstrated high IL-10 (tolerogenic) and low TNF-α (inflammatory) secretion. After further optimizing these combinations, we identified two combinations that generate robust tolDCs from BMDCs ex vivo. We further show that these PPI-tolDCs can also generate antigen specific Tregs but do not increase overall Treg populations. Discussion These second-generation PPI formulations have significant potential to generate robust tolDCs and strong antigen specific Tregs.
Changes in Microbiota Composition during the Anaerobic Digestion of Macroalgae in a Three-Stage Bioreactor

The use of microalgae as a raw material for biogas production is promising. Macroalgae were mixed with cattle manure, wheat straw, and an inoculant from sewage sludge. Mixing macroalgae with co-substrates increased biogas and methane yield. The research was carried out using a three-stage bioreactor. During biogas production, the dynamics of the composition of the microbiota in the anaerobic chamber of the bioreactor was evaluated. The microbiota composition at different organic load rates (OLRs) of the bioreactor was evaluated. This study also demonstrated that in a three-stage bioreactor, a higher yield of methane in biogas was obtained compared to a single-stage bioreactor. It was found that the most active functional pathway of methane biosynthesis is PWY-6969, which proceeds via the TCA cycle V (2-oxoglutarate synthase). Microbiota composition and methane yield depended on added volatile solids (VSadded). During the research, it was found that after reducing the ORL from 2.44 to 1.09 kg VS/d, the methane yield increased from 175.2 L CH4/kg VSadded to 323.5 L CH4/kg VSadded.
Evaluation of Local Site Conditions Using Ambient Seismic Noise Recordings: A Case Study from Ankara, Turkey

This paper aims to investigate the site response of the sediment characteristics and to perform seismic hazard studies in Ankara, Turkey through conducting short-period noise recordings of microtremor measurements at 352 project site locations on the Upper Pliocene to Pleistocene fluvial and particularly Quaternary alluvial and terrace sediments in the Ankara basin. The spectral ratios relative to a firm site reference station (SSR) and the spectral ratio between the horizontal and vertical components (HVSR) of the microtremor measurements at the ground surface have been used to estimate the fundamental periods and the amplification factors of the site. The results of this study identified three main factors that influence site response, namely, the age of the near-surface deposits, the sediment thickness and the non-linear soil behavior. In particular, the HVSR results showed that the variation of the fundamental period map agreed well with the maximum value of the amplification as well as with the seismic sediment characteristics that provided satisfactory estimates of the site response of soft deposits.
A Review and Comparative Analysis of Various Encryption Algorithms

Now days, Data security is very challenging issue that touches many areas including computers and communication. Recently, we came across many attacks on cyber security that have played with the confidentiality of the users. These attacks just broke all the security algorithms and affected the confidentiality, authentication, integrity, availability and identification of user data. Cryptography is one such way to make sure that confidentiality, authentication, integrity, availability and identification of user data can be maintained as well as security and privacy of data can be provided to the user. Encryption is the process of converting normal data or plaintext to something incomprehensible or cipher-text by applying mathematical transformations or formulae. These mathematical transformations or formulae used for encryption processes are called algorithms. We have analysed ten data encryption algorithms DES, Triple DES, RSA, AES, ECC, BLOWFISH, TWOFISH, THREEFISH, RC5 and IDEA etc. Among them DES, Triple DES, AES, RC5, BLOWFISH, TWOFISH, THREEFISH and IDEA are symmetric key cryptographic algorithms. RSA and ECC are asymmetric key cryptographic algorithms. In this paper, we have analysed various encryption algorithms on the basis of different parameters and compared them to choose the best data encryption algorithm so that we can use it in our future work.
¿Tramposo e injusto? Entonces, es humano. Robots sociales educativos y ética sintética

La educación comienza a hacer uso de la inteligencia artificial emocional a través de robots educativos antropomorfizados. La evidencia respalda que los estudiantes (hombres y mujeres) son capaces de crear vínculos emocionales con estos agentes. Sin embargo, cada vez se están encontrando más casos de desinhibición abusiva en este tipo de interacciones, como degradaciones racistas o sexistas, abuso de poder y violencia. Algunos investigadores alertan sobre las consecuencias negativas que este tipo de conductas pueden tener a largo plazo, tanto para la educación ética de los estudiantes como para los robots que aprenden de estas conductas. A pesar de su relevancia desde una perspectiva social y educativa, existen pocos estudios que intenten comprender los mecanismos que subyacen a estas prácticas inmorales o colectivamente dañinas. El objetivo de este artículo es revisar y analizar las investigaciones que han tratado de estudiar el comportamiento antiético del ser humano a través de su interacción con los robots sociales antropomórficos. Se realizó un estudio bibliométrico descriptivo siguiendo los criterios de la declaración PRISMA. Los resultados muestran que, bajo ciertas circunstancias, la antropomorfización y la atribución de intencionalidad a los agentes robóticos podría ser desventajosa, provocando actitudes de rechazo, deshumanización e incluso violencia. Sin embargo, una visión más realista tanto de las capacidades y limitaciones de estos agentes como de los mecanismos que guían la conducta humana podría ayudar a aprovechar el gran potencial de esta tecnología para promover el desarrollo moral y la conciencia ética de los estudiantes.