Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a fully optical neural network

The work described in this thesis is part of a Laser Neural Network project that aims at realising a neural network in optics. A possible application for such an optical neural network is a fully optical cross connect in a telecommunication network. The injection seeding neuron is a new concept for a fully optical neuron, the basic element of a neural network. In this thesis the concept of the injection seeding neuron is described and verified both theoretically and experimentally. The injection seeding neuron realises the non-linear function, necessary for the implementation of a neuron, by injecting light into a semiconductor laser. The output power of the laser shows a sigmoid like behaviour as function of the injected power. When this injected power is made proportional to a weighed sum of inputs, an optical neuron is realised. By applying controllable feedback to the laser, the threshold of the neuron can be influenced. Simulations are done to examine the behaviour of the injection seeding neuron theoretically. The model used for these simulations is the multi-mode rate equations model. Procedures to find the steady state solution and the time dependent solution are described. The steady state solution is used to examine the non-linear function of the neuron. The results from these simulations are consistent with the principle of operation. An experimental setup is built to verify the principle of operation of the injection seeding neuron. A source laser is wavelength tunable and provides the signal to be injected in the neuron. A neuron laser has controllable feedback for each mode of the laser diode individually and a way of injecting external light. The experiments done with this setup show that the injection seeding neuron indeed shows a sigmoid like behaviour, but further experiments are necessary for a complete verification of the operation principle. A more stable experimental setup is necessary for future experiments and recommendations are made on how this can be achieved.
Cutoff points of T1 rho/T2 mapping relaxation times distinguishing early-stage and advanced osteoarthritis

Introduction The histopathology grading system is the gold standard post-operative method to evaluate cartilage degeneration in knee osteoarthritis (OA). Magnetic resonance imaging (MRI) T1 rho/T2 mapping imaging can be used for preoperative detection. An association between histopathology and T1 rho/T2 mapping relaxation times was suggested in previous research. However, the cutoff point was not determined among different histopathology grades. Our study aimed to determine the cutoff point of T1 rho/T2 mapping. Material and methods T1 rho/T2 mapping images were acquired from 80 samples before total knee replacements. Then the histopathology grading system was applied. Results The mean T1 rho/T2 mapping relaxation times of 80 samples were 39.17 ms and 37.98 ms respectively. Significant differences were found in T1 rho/T2 mapping values between early-stage and advanced OA (p < 0.001). The cutoff point for T1 rho was 33 ms with a sensitivity of 94.12 (95% CI: 80–99.3) and a specificity of 91.30 (95% CI: 79.2–97.6). The cutoff point for T2 mapping was suggested as 35.04 ms with a sensitivity of 88.24 (95% CI: 72.5–96.7) and specificity of 97.83 (95% CI: 88.5–99.9). After bootstrap simulation, the 95% CI of the T1 rho/T2 mapping cutoff point was estimated as 29.36 to 36.32 ms and 34.8 to 35.04 ms respectively. The area under the PR curve of T1 rho/T2 mapping was 0.972 (95% CI: 0.925–0.992) and 0.949 (95% CI: 0.877–0.989) respectively. Conclusions The cutoff point of T1 rho relaxation times, which was suggested as 33 ms, could be used to distinguish early-stage and advanced OA.
Comparative Phytochemical and Antimicrobial Properties of Two Cultivars of Catharanthus roseus L. {G.} Don on Escherichia coli and Candida albicans

Medicinal plants have served as sources of medicine to treat and suppress the diseases, because many pathogens are gaining resistance to the current synthetic drugs. In addition, high cost and adverse side effects are commonly associated with popular Synthetic drugs. Therefore, there is need for continuous search for new drugs in order to overcome this emerging resistance. Plants synthesize bioactive compounds which are of great potential in agriculture, antimicrobial and anti-insect activity. The concentration of bioactive compounds in each plant species depends on the environmental conditions, age of the plant, relative humidity of harvested materials and method of extraction. Little is known on the phytochemical and antimicrobial potential of Alba and Rosea cultivars of Catharanthus roseus ethanol extracts. The leaves of Alba and Rosea cultivars were investigated for their phytochemical and antimicrobial properties. The study was conducted at Maseno University, Kenya. Plant Leaves were collected around Maseno University. Leaves of Alba and Rosea cultivars of Catharanthus roseus were air-dried in the shade, thereafter crushed into powder and ethanol extraction done using the Rotary evaporator. Antimicrobial activity of the pathogenic microorganisms was Candida albicans and Escherichia coli. Disc diffusion method was used for antimicrobial tests. concentrations of ethanol leaf extracts consisted of 2.5, 5and 7.5 mg/mL with three replications. Data on growth inhibition were subjected to analysis of variance (ANOVA) . Phytochemical analysis revealed the presence of tannins, flavonoids, terpenoids, saponins, alkaloids and phenols except steroids and glycosides. The ethanol leaf extracts were active against Candida albicans and and Escherichia coli. Alba leaves extracts showed higher inhibitory zones compared to Rosea leaves. The observed differences in antimicrobial activity could be due to differences in cell wall synthesis, structure and composition. The results of present study further confirm the use of these plants traditionally for the treatment of different ailments.
Component-based Error Detection of P4 programs

P4 is a domain-specific language to develop the packet processing of network devices. These programs can easily hide errors, therefore we give a solution to analyze them and detect predefined errors in them. This paper shows the idea, which works with the P4 code as a set of components and processes them one by one, while calculating their pre- and postconditions. This method does not only detect errors between the components and their connections, but it is capable to reveal errors, which are hidden in the middle of a component. The paper introduces the method and shows its calculation in an example.
People’s Perceptions about the Socio-Economic and Environmental Impact of Coastal Green Belt in Bangladesh

Understanding the perceptions and attitudes of local people towards afforestation is crucial for successful afforestation. To better understand the people's perceptions about the coastal green belt, we surveyed the Satkhira and Bhola districts during January 2021. A questionnaire survey of 200 respondents was conducted by a purposive and random sampling technique to obtain quantitative data. On the other hand, two Focus Group Discussions (FGDs) were performed to obtain qualitative data based on the diversity of age, sex, education, and occupation variables. We used to evaluate the respondent’s perceptions about the coastal green belt by 5-point Likert scale data. The majority portion of the people in our study area was poor and their profession was fisherman and housewife among males and women respectively. The majority of them agreed that the green belt helped them in numerous ways, to rear their cattle and built a house near the green belt, by promoting the growth of crops, and further protect them from different natural calamities by reducing wind velocity. Shelterbelts greatly enhance tourism in that area, improved communication facilities, and increase their socio-economic condition and values. Shelterbelt has long-term benefits, peoples have positive attitudes towards shelterbelt and they seek training to maintain this shelterbelt smoothly. Respondents wanted fruit tree species as shelterbelt species near the embankment. This information can be used for policy formulation in terms of successful plantation by considering people’s attitudes, which may work in both mitigation and adaptation of climate change in the coastal remote areas of Bangladesh.
Negative Social Interaction and Psychological Well-Being on Female Basketball Athletes

Exercise can maintain a person's level of psychological well-being or also known as Psychological well-being (PWB), but on the other hand, it can also have a negative effect. Negative Social Interaction is one of the factors that have a negative effect on individuals from their environment during exercise. In terms of gender, women and sports are interrelated even though women have long been underestimated in sports, especially in sports that are considered masculine sports. This study examines the negative social interactions experienced by athletes in basketball. In basketball, negative social interactions can occur due to the assumption that basketball is a masculine sport, besides, that women are considered weaker than men. So this study aims to analyze the impact of Negative Social Interaction obtained by female basketball athletes on the Psychological well-being (PWB) of athletes. The instruments used were Negative Social Exchange (NSE) and Psychological Well-being Scale questionnaires as well as short interviews given to 24 female basketball athletes at the University of Indonesia Education. The results showed that NSI had a significant relationship with the PWB of female basketball athletes. The coach is one individual who plays a very important role in the condition of the athlete's PWB. Not only can it have a negative impact, but NSI can also be turned into a motivation that can encourage athletes to fight harder in the future. The author provides suggestions for coaches who handle female basketball athletes to be more careful when interacting with athletes and for basketball connoisseurs to better organize themselves in expression when enjoying this sport.
Formulation and Evaluationation of Anti-Alzheimer Drug MEM HCL Nanogel

The object of present study was to formulate and evaluate MEM HCl loaded thermo sensitive in situ nanogel for 08 nasal delivery formulations. In present project a novel drug delivery system i.e. in situ polymeric gel was designed in the manner that the gel load MEM HCl in better concentration and it also incorporates penetration enhancer as a way to enhance the absorption of release drug from gel to the systemic circulation. In this research work Different Nanoparticles were (NP) prepared, using ionotropic gelation method with slight medication in which chitosan (0.4% w/v) was dissolved in aqueous acetic acid solutions (1 % v/v) (pH 6.1), while TPP (0.1% w/v) was dissolved in deionized water. Dried nanoparticles are incorporated with in situ gel. In situ gel was prepared by cold method using the solutions of Poloxamer-188 and Carbopol-934. From this study, it is concluded that, among all formulations prepared, NG8 was the best optimized formulation. Prepared gel can be used as promising nasal drug delivery system for the anti-Alzheimer drug MEM HCl, which enhance nasal residence time owing to increased viscosity and mucoadhesive characteristics; furthermore, it also exhibited a permeation enhancing effect.
Recent Advances in Topical Nanotechnological Strategies for Treatment of Alopecia

Alopecia can be characterized as a hair-related disorder leading to decreased hair growth, especially on the scalp. It has affected approximately 2% of the world population. The hair loss may emerge due to genetic or environmental factors like stress, infectious agents, tensile forces or medications. The pathogenetic pathway of induction of alopecia comprises of different factors, but exploitation of hair follicle immune privilege system (HFIP) is the most prominent one. Alopecia can be classified into two types: scarring alopecia or non-scarring alopecia. Although, many conventional dosage forms have been formulated against alopecia, but they have been associated with different adverse effects such as weight gain, palpitations, itching etc. In order to overcome the adverse effects associated with conventional dosage forms, the topical nano-formulations have been used for the treatment of dermatological disorders. Nano-formulations are known to increase the contact time of drug with the target site, thereby, achieving sustained and controlled release of the drug leading to enhanced therapeutic compliance. This review provides an in-depth study of developed nanocarriers like liposomes, nano-emulsions, ethosomes, solid lipid nanoparticles (SLNs), nanostructured lipid carriers (NLCs) along with their composition, method of preparation, results obtained after formulation, suitable use and along with their potential use in treatment of different forms of alopecia.
Exploring Self-Compassion in Older Adults: a systematic review

The present study sought to synthesize available evidence on the role of self-compassion in elders aged 60 or above and its potential implications during the process of aging. A systematic review was conducted in order to search for empirical scientific articles with quantitative, qualitative, or mixed methods with a focus on self-compassion in samples with elders aged 60 years or more. Searches were carried out in the Psycinfo, Scopus, Pubmed, Embase and Cochrane databases, without limit of time. After the application of including and excluding criteria, 11 articles were considered eligible for this review, with all presenting good methodological quality. Self-compassion was found to be a promising skill that can promote healthy psychosocial aging, enabling a better adjustment to changes associated with aging. Such results contribute to the development of new mental health intervention protocols, especially in the field of psychogerontology. There is a need for more longitudinal research and investment in the development of specific interventions for this age group.
Diversity and biological activity of culturable endophytic bacteria associated with marigold (Calendula officinalis L.)

Endophytes colonizing plant tissue play an essential role in plant growth, development, stress tolerance and plant protection from soil-borne diseases. In this study, we report the diversity of cultivable endophytic bacteria associated with marigold (Calendula officinalis L.) by using 16S rRNA gene analysis and their plant beneficial properties. A total of 42 bacterial isolates were obtained from plant tissues of marigold. They belonged to the genera Pantoea, Enterobacter, Pseudomonas, Achromobacter, Xanthomonas, Rathayibacter, Agrobacterium, Pseudoxanthomonas, and Beijerinckia. Among the bacterial strains, P. kilonensis FRT12, and P. rhizosphaerae FST5 showed moderate or vigorous inhibition against three tested plant pathogenic fungi, F. culmorum, F. solani and R. solani. They also demonstrated the capability to produce hydrolytic enzymes and indole-3-acetic acid (IAA). Five out of 16 isolates significantly stimulated shoot and root growth of marigold in a pot experiment. The present study reveals that more than half of the bacterial isolates associated with marigold (C. officinalis L.) provided antifungal activity against one or more plant pathogenic fungi. Our findings suggest that medicinal plants with antimicrobial activity could be a source for selecting microbes with antagonistic activity against fungal plant pathogens or with plant growth stimulating potential. These isolates might be considered as promising candidates for the improvement of plant health.
Safe-haven assets for U.S. equities during the 2020 COVID-19 bear market

The recent stock market downturn is differentiated from the previous ones as it is due to an economic, rather than a financial occurrence (the COVID-19 Pandemic). The purpose of our study is to examine gold, bitcoin, and U.S. Treasury bonds as a safe haven during the COVID-19 bear market. Unlike many studies that support gold as a traditional safe haven for stocks, our study finds that bitcoin and Treasury bonds perform better as a safe haven than gold during the recent COVID-19 bear market.
ASLBoT – Assistive Sign Language Bot Translator

—Human interpreters are relied upon by the sign language community for interpretation of the English language. However, this method of used in order to translate English to the appropriate sign language results in high costs, reliability, and availability issues. With major advances in animation programing and machine translation new avenues may now be taken help this community obtain the reliable and responsive sign language translations. Herein, we provide a novel approach to translating English into the animated sign language version providing a low-cost alternative. This novel approach is composed of using small corpora and our own developed training framework. A neural machine translation (NMT) model is trained to translate from English to ASL-Gloss and a Unity script generates the corresponding sign language animation tied to the ASL-Gloss translation. ASLBoT aims to be the first product on the market that can translate between English to American Sign Language while providing a cost-effective, easy-to-use and reliable translation for people with hearing disabilities.
Modelling invasive plant alien species richness in Tenerife

Biological invasions are one of the major threats to biodiversity, especially on islands where the number of endemic species is the highest despite their small area. In the Canary Islands, the relationships among invasive alien species (hereafter IAS) and their environmental and anthropogenic determinants have been thoroughly described but robust provisional models integrating species spatial autocorrelation and patterns of IAS communities are still lacking. In this study, we developed a Generalised Linear Spatial Model for Invasive Alien Species Richness (IASR) under a Bayesian framework, using a methodological approach that encompass GIS and geostatistical analysis. In this study, we hypothesised that the inclusion of spatial autocorrelation can improve model performance thus obtaining more IASRreliable predictions. In addition, this method provides uncertainty maps that prioritize areas where further sampling eff orts are needed. Our model showed that IASR in Tenerife is mainly driven by a combination of anthropogenic and natural processes, highlighting favourable conditions for IAS from the coastline to about 800 m a.s. l. , especially on the windward humid aspect. Among anthropogenic factors, a clear positive relationship between road kernel density estimation and IASR was found. Indeed, road density has recently increased especially in low to mid altitudinal zones on the Canary Islands, strictly associated with urban expansion and it has been widely demonstrated to be one of the main IAS pathways. Hence, higher road density can be related to increased ‘propagule pressure’ which is, together with source of disturbance, one of the most important factors explaining richness in alien species invasion success. Our main conclusions highlight the importance of considering spatial autocorrelation and researchers’ prior knowledge to increase the predictive power of statistical models. From a practical perspective, these models and their related uncertainty, will serve as important management tools highlighting those portions of territories that will be more prone to biological invasions and where monitoring eff orts should be directed.
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Copy right Idea Grou p Inc. Copy right Idea Grou p Inc. ABSTRACT This paper reports on an action research study that used the Strategic Choice method. The method was used to support the prioritisation of information systems with respect to enhancing these systems within a public sector Health Department. Such decisions are notoriously complex, fuzzy, time consuming and political for stakeholders. The results of this study indicate that the Strategic Choice method offers the potential to reduce time commitment for stakeholders yet do so in a satisfactory manner. Using Strategic Choice To Make Information Systems Decisions Quickly
Spectral sequences for computing persistent homology of digital images ∗

Persistent homology and spectral sequences are two Algebraic Topology tools which are deﬁned by means of a ﬁltration and can be applied to study topological properties of a space at diﬀerent stages. Both concepts are deeply related, and this relation allows us to use some previous programs developed for computing spectral sequences of ﬁltered complexes to determine now persistent homology. In particular, spectral sequences can be applied to compute persistent homology of digital images, which will allow us to determine relevant features, that will be long-lived on contrast with the “noise” which will be short-lived.
A BI Tool for the AI based BI Framework for Churn Prediction of the Telco Industry and Getting the Prescriptive Decisions by Minimizing the Churning Customers

Information has become an integral in both individuals and the business and a large amount of digital information in new types is being generated every moment from different data capturing devices. As today we are living in an on-demand on-command world, the data analytics is playing an increasingly critical role for making full use of it in most of the business organizations. Different forms of analytics can be found, but they don't provide the best perspective or provide the right information. To turn a company into a profitable operation, top management relies heavily on decisions, which are in turn based on the type of knowledge available. Hence, we are going to introduce a new AI based BI Framework which can be used by different businesses, industries and service providing companies for predictions and to get the prescriptive decisions. In this paper, we are going to introduce a Business Intelligence tool for the AI based BI framework to analyse the big data set and to extract the required data and create a predictive model for making the prescriptive decisions for the business. This BI tool is sophisticated, user-friendly, cost-effective and comprehensive data-driven business intelligence tool for efficient prediction, and it can be used to analyse the big data sets of the industry. The BI tool must be trained using a data set of the same industry before it is used with the large data set. The tool first extracts the data set, transform the extracted data set to the required format and then it is loaded to the BI tool to select the suitable algorithm. The tool has a set of pre-defined algorithms and the loaded data set is analysed using these algorithms and get the percentage of accuracy in each algorithm we introduced with the tool. Then the end-users will be able to select the suitable algorithm for the data set and then it can be used with the big data set of the similar industry. The AI based BI framework provides the facility for many cross industries and businesses. In this work, we focused on the Telco Industry and we implemented our novel Business Intelligence tool for that industry. Because they are facing problems in finding hidden patterns in their large chunk of data which may have a big impact on their decision-making process. Most telecommunication companies are suffering from churn (customer leaving the service) of their customers day by day due to high competition within the competitor telco companies. This churning will badly effect to the huge financial losses and survival of such companies. The experiment result showed more than 90% accuracy of selecting the right churn customers and the tool helped to make the prescriptive decisions to the top management to minimize churning.