Profitability, Inputs Elasticities And Resource-Use Efficiency In Small Scale Cowpea Production In Niger State, Nigeria

The study examined profitability, inputs elasticities and resource-use efficiency in small scale cowpea production in Niger State, Nigeria. The primary data for the study were obtained using structured questionnaire administered to one hundred randomly sampled farmers fromtwo Local Government Areas. Descriptive statistics, gross margin, net farm income, gross ratio, operation ratio and return on capital investment and production function using regression model were used to analyze the data. The result showed that estimated gross margin; net farm income; gross ratio; operating ratio; and return on capital investment gives an estimated values of N28,063.63 per hectare, N25,550.50 per hectare, 0.46, 0.30 and 1.46 respectively.The regression model estimated revealed that double log (Cobb Douglass) as the lead equation with the value of coefficient of determination (R2) 0.765, indicated that 76.5% of the variation in output of cowpea production was explained by the inputs included inregression model. The F-ratio estimated as 16.369 was significant at 1% level of probability. The result also showed that land (X1), labour (X2) and fertilizer (X5) were significant at 1%, level of probability while Seed (X3) was significant at level of probability. Elasticity of production (return to scale) estimated as 14.383 implies that the production is characterizedby increasing return to scale Estimated efficiency ratio( r) shows that the resources used were not efficiently utilized. It was therefore recommended that farm inputs, especially improved seeds and agrochemicals, should be supplied to farmers at the right time and at cost that is within their reach. Also extension agents should be provided to disseminate research findings to cowpea farmers on modern technology

Type 2 segmental darier's disease in a twelve-year old Nigerian male - A case report

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Traditional Underground Grain Storage in Clay Soils in Sudan Improved by Recent Innovations

In the central clay plain of the Sudan, traditional subsistence farmers and small farmers that also produce for local markets want to keep the region near food self-sufficiency. They combine annual production of sorghum with underground pit storage of part of the harvest. With increasing climate variability this food security is coming more and more under pressure. Farmers recently experimented with pit innovations that would allow storage for more than one season. These innovations were quantified and further improvements were suggested. It was found that in the most abundantly occurring cracking clay soils, wide shallow pits, using thick chaff linings, with wider above ground soil caps, are most suitable for longer term storage

Female Genital Tuberculosis Among Infertile Women and Its Contributions to Primary and Secondary Infertility: A systematic review and meta-analysis

Female genital tuberculosis (FGTB) is an infectious widespread disease among young women. This meta-analysis study aimed to investigate the prevalence of Female Genital Tuberculosis among infertile women and its contribution to primary and secondary infertility. A PubMed, MEDLINE, world cat log, Lens.org, direct Google search, Google Scholar, and Researchgate, from 1971 to July 17, 2021, were searched using the keywords; prevalence, epidemiology, urogenital tuberculosis, FGTB, infertile women, infertility complaints, and FGTB testing methods. Data extracted and meta-analysis was performed. 42 studies were selected with a total of 30918 infertile women. Of these, the pooled prevalence of FGTB was 20% (15-25%; 95%CI; I2 99.94%), and the prevalence of overall infertility, primary infertility, and secondary infertility among FGTB-population were 88%, 66% and 34%, respectively. The proportion of FGTB is remarkable among infertile women globally. The biggest burden of the disease is presented in the low-income countries followed by the lower middle-income, and upper-middle-income countries

Optimized and efficient image-based IoT malware detection method

With the widespread use of IoT applications, malware has become a difficult and sophisticated threat. Without robust security measures, a massive volume of confidential and classified data could be exposed to vulnerabilities through which hackers could do various illicit acts. As a result, improved network security mechanisms that can analyse network traffic and detect malicious traffic in real-time are required. In this paper, a novel optimized machine learning image-based IoT malware detection method is proposed using visual representation (i.e., images) of the network traffic. In this method, the ant colony optimizer (ACO)-based feature selection method was proposed to get a minimum number of features while improving the support vector machines (SVMs) classifier’s results (i.e., the malware detection results). Further, the PSO algorithm tuned the SVM parameters of the different kernel functions. Using a public dataset, the experimental results showed that the SVM linear function kernel is the best with an accuracy of 95.56%, recall of 96.43%, precision of 94.12%, and F1_score of 95.26%. Comparing with the literature, it was concluded that bio-inspired techniques, i.e., ACO and PSO, could be used to build an effective and lightweight machine-learning-based malware detection system for the IoT environment

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

BACKGROUND: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review. METHODS: In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software. RESULTS: Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; [Image: see text]=0.91, Quickscore; [Image: see text]=0.92). CONCLUSIONS: The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials

Development of new all-optical signal regeneration technique

All-optical signal regeneration have been the active research area since last decade due to evolution of nonlinear optical signal processing. Existing all-optical signal regeneration techniques are agitated in producing low Bit Error Rate (BER) of 10-10 at below than -10 dBm power received. In this paper, a new all-optical signal regeneration technique is developed by using phase sensitive amplification and designed optical phase locked signal mechanism. The developed all-optical signal regeneration technique is tested for different 10 Gb/s Differential Phase Shift Keying degraded signals. It is determined that the designed all-optical signal regeneration technique is able to provide signal regeneration with noise mitigation for degraded signals. It is analyzed that overall, for all degraded test signals, average BER of 10-13 is achieved at received power of -14 dBm. The designed technique will be helpful to enhance the performance of existing signal regeneration systems in the presence of severe noise by providing minimum BER at low received power

Molecular Detection of Leishmania spp. in Skin and Blood of Stray Dogs from Endemic Areas of Cutaneous Leishmaniasis in Saudi Arabia

Dogs can act as reservoirs of canine leishmaniasis, caused by Leishmania species. The aims of this study were to determine the prevalence of canine leishmaniasis using a PCR technique among stray dogs living in three provinces of Saudi Arabia, Riyadh, Al-Ahsa Oasis and Al-Qaseem, here the disease is endemic; and to identify and document different Leishmania to species levels Methods: This cross-sectional investigation was conducted, from Mar 2016 to Apr 2018, in three parts of Saudi Arabia: Central province (Riyadh), Eastern province (Al-Ahsa Oasis) and Al-Qaseem province. Blood samples were collected from 526 dogs; 40 presented cutaneous nodules so were suspected clinically of cutaneous leishmaniasis. Biopsy tissue collections and parasite cultures were performed. A generic kDNA was performed using different primers for Leishmania differentiation. Results: All blood samples were negative for Leishmania infantum infection by molecular analysis, though forty dogs had thick cutaneous lesions in different parts of their body. Four dogs’ skin lesions were associated with dermatitis, splenomegaly and lymphadenomegaly. Parasite culture was used to diagnose cutaneous leishmaniasis, identifying 31/40 (77.5%) positive samples. Overall, of 526 samples, the prevalence of L. major and L. tropica was found to be 4% and 1.9%, respectively. Gender and age had a significant effect on Leishmania prevalence: (P=0.0212 and0.0357), respectively. Conclusion: This was the first molecular study of dog leishmaniasis from Saudi Arabia of dogs confirmed to have cutaneous leishmaniasis. Further epidemiological and molecular investigations of domestic and wild canine infections with L. major, L. tropica and L. infantum in endemic and nonendemic areas of Saudi Arabia are required, for leishmaniasis control

3D printed superparamagnetic stimuli-responsive starfish-shaped hydrogels

Magnetic-stimuli responsive hydrogels are quickly becoming a promising class of materials across numerous fields, including biomedical devices, soft robotic actuators, and wearable electronics. Hydrogels are commonly fabricated by conventional methods that limit the potential for complex architectures normally required for rapidly changing custom configurations. Rapid prototyping using 3D printing provides a solution for this. Previous work has shown successful extrusion 3D printing of magnetic hydrogels; however, extrusion-based printing is limited by nozzle resolution and ink viscosity. VAT photopolymerization offers a higher control over resolution and build-architecture. Liquid photo-resins with magnetic nanocomposites normally suffer from nanoparticle agglomeration due to local magnetic fields. In this work, we develop an optimised method for homogenously infusing up to 2 wt % superparamagnetic iron oxide nanoparticles (SPIONs) with a 10 nm diameter into a photo-resin composed of water, acrylamide and PEGDA, with improved nanoparticle homogeneity and reduced agglomeration during printing. The 3D printed starfish hydrogels exhibited high mechanical stability and robust mechanical properties with a maximum Youngs modulus of 1.8 MPa and limited shape deformation of 10% when swollen. Each individual arm of the starfish could be magnetically actuated when a remote magnetic field is applied. The starfish could grab onto a magnet with all arms when a central magnetic field was applied. Ultimately, these hydrogels retained their shape post-printing and returned to their original formation once the magnetic field had been removed. These hydrogels can be used across a wide range of applications, including soft robotics and magnetically stimulated actuators