Varicocele Surgery Improves Sperm Count in Infertile Oligospermic Patients and so Improves Fertility; A Study in a Tertiary Care Hospital

The objective of this study was to find out the role of varicocele surgery in oligospermic infertile patients. It was a prospective and descriptive study carried out in Surgical Unit-I, Abbasi Shaheed Hospital and Karachi Medical and Dental College (KMDC), Karachi from April 2004 to March 2014. In this study, all patients of infertility due to low sperm count having bilateral varicocele were included while those patients having azoospermia or patients with unilateral varicocele were excluded. All patients were clinically assessed for bilateral varicocele and confirmed by ultrasonography of scrotum and relevant investigations were done. Patients were prepared for varicocele surgery and ligation of pampiniform plexus done. Semenanalysis were done during follow up and results were analyzed on SPSS version 14. Total fifty seven patients (n=57) were included in which age range was 20 to 30 years in 33.3%, 31 to 40 years in 42.1%, 41 to 50 years in 19.3% and 51 years to onwards in 05.3% patients only. Chronic smoking was found in 68.4% patients while 31.6% were nonsmokers. Normal testes was found in 77.19% while 22.81% had smaller (atrophied) testes. Very low sperm count was in 15.79%, 50.88% had low sperm count and 33.33% had near normal sperm count. All patients were operated for bilateral varicocele and discharged. Follow-up semen analysis showed improvement and semen analysis became normal in 19.3% after six months, 21.05% after nine months and 36.84% after one year of surgery while 22.81% had no improvement even after one year of surgery. Thus, patients with bilateral varicocele having low sperm count showed improvement in sperm count after varicocele surgery and so infertile patients may become fertile after varicocele surgery

Adaptive and Concurrent Garbage Collection for Virtual Machines

An important issue for concurrent garbage collection in virtual machines (VM) is to identify which garbage collector (GC) to use during the collection process. For instance, Java program execution times differ greatly based on the employed GC. It has not been possible to identify the optimal GC algorithms for a specific program before exhaustively profiling the execution times for all available GC algorithms. In this paper, we present an adaptive and concurrent garbage collection (ACGC) technique that can predict the optimal GC algorithm for a program without going through all the GC algorithms. We implement this technique in the Java virtual machine and test it using standard benchmark suites. ACGC learns the algorithms’ usage pattern from different training program features and generates a model for future programs. Feature generation and selection are two important steps of our technique, which creates different attributes to use in the learning step. Our experimental evaluation shows improvement in selecting the best GC. Additionally, our approach is helpful in finding better heap size settings for improved program execution

Efektifitas Implementasi Smm Iso 9001:2008 Pada Smk Negeri Di Kota Singaraja

Penelitian ini bertujuan untuk memberikan gambaran tentang : 1) efektivitas penerapan Sistem Manajemen Mutu (SMM) ISO 9001:2008 pada SMK Negeri di kota Singaraja dilihat dari konteks, masukan, proses, dan hasil penerapan; 2) kendala-kendala yang dihadapai dalam melaksanakan SMM ISO 9001:2008 pada SMK Negeri dikota Singaraja serta alternatif pemecahannya. Penelitian ini dilaksanakan pada SMK Negeri dikota Singaraja pada tahun pelajaran 2013/2014, dengan menggunakan model evaluasi CIPP dari Stufflebeam yang melibatkan 354 responden. Variabel konteks yang terdiri dari sub variabel SMM sekolah dan keterlibatan komite sekolah. Variabel masukan terdiri dari sub variabel manajemen sekolah dan keterlibatan dunia USAha/dunia industri (DU/DI). Variabel proses yang terdiri dari sub variabel pemeliharaan dan pengadaan sarana pendidikan, kegiatan belajar mengajar oleh guru, dan kegiatan belajar mengajar siswa. Variabel produk dengan sub variabel nilai ujian tahun pemelajaran 2013/2014 (UN, US dan UK). Metode kuesioner digunakan untuk menjaring data SMM sekolah, manajemen sekolah dengan responden semua staff manajemen, keterlibatan dunia USAha/dunia industri (DU/DI), dan proses pembelajaran siswa. Metode wawancara untuk menjaring data keterlibatan komite sekolah. Metode observasi untuk menjaring data kegiatan belajar mengajar oleh guru. Metode studi dokumen untuk menjaring data SMM sekolah, pengadaan dan pemeliharaan sarana pendidikan, dan nilai ujian. Hasil penelitian menunjukkan bahwa : 1) efektif dilihat dari variabel konteks dengan frekuensi kategori positif 54.286% untuk SMM dan frekuensi kategori positif 75% untuk keterlibatan komite; 2) efektif dilihat dari variabel masukan dengan frekuensi kategori positif 57.6271% untuk manajmen sekolah dan frekuensi kategori positif 52.041% untuk keterlibatan DU/DI; 3) kurang efektif dilihat dari variabel proses dengan frekuensi kategori negatif 63.1578% untuk pemeliharaan dan pengadaan saran pendidikan, dan frekuensi kategori positif 57.4713% untuk kegiatan belajar mengajar oleh guru, dan frekuensi kategori positif 52.308% untuk kegiatan belajar mengajar siswa; 4) efektif dilihat dari variabel produk dengan frekuensi kategori positif 52.055.00% untuk nilai ujian. Bertolak dari hasil penelitian tersebut dapat direkomendasikan: 1) meningkatkan koordinasi setiap kebijakan baru; 2) penerapan SMM perlu disosialisasikan secara terus menerus pada setiap kesempatan; 3) meningkatkan komitmen warga sekolah untuk menerapakan SMM; 4) meningkatkan koordinasi dengan industri menuju Manajemen Partisipatif; 5) memberikan tugas dan tanggungjawab yang jelas kepada kepala program

Heavy Metal Levels in Vegetables and Soil Cultivated with Industrial Wastewater from Different Sites of Chunian and Jamber, District, Kasur

In human diet, vegetables play important role to maintain the physiological conditions. Due to anthropogenic activities and pollution, the food items become contaminated. The present study was performed to evaluate the level of heavy metals in the vegetables irrigated with wastewater across Chunian and Jamber, district, Kasur. Level of heavy metals from the study area like Zinc, Lead and chromium in the soil, water and vegetables was compared. The four sites of each city and 10 vegetables e.g. potato, radish, carrot, fenugreek, spinach, tomato, Onion, Turnip, Cauliflower, Pangalo were selected to conduct the experiment. The vegetables were irrigated with industrial wastewater and the concentration of heavy metals was measured by the atomic absorption spectrophotometer (AAS). We concluded that the level of heavy metals was beyond the FAO limits in irrigated water due to industrial waste. In Jamber and Chunian, the level of Zn and Pb was high and beyond the FAO safe limits in the all water sample, the level of Cr was much higher only in the water sample of one site from Jamber. The concentration of zinc was higher in soil samples as compared to lead and chromium. Zn and Pb in vegetables of study area were labeled as priority pollutants but this concentration was within the safe limits set by FAO. However, constant inspection of heavy metals is recommended to avoid accumulation in the food chain and thus avoid human health risks. Keywords: Atomic absorption spectrophotometer, Heavy metals, Industrial wastewater, Vegetables

Benign versus malignant hepatic nodules: MR imaging findings with pathologic correlation

According to the currently used nomenclature, there are only two types of hepatocellular nodular lesions: regenerative lesions and dysplastic or neoplastic lesions. Regenerative nodules include monoacinar regenerative nodules, multiacinar regenerative nodules, cirrhotic nodules, segmental or lobar hyperplasia, and focal nodular hyperplasia. Dysplastic or neoplastic nodules include hepatocellular adenoma, dysplastic foci, dysplastic nodules, and hepatocellular carcinoma (HCC). Many of these types of hepatic nodules play a role in the de novo and stepwise carcinogenesis of HCC, which comprises the following steps: regenerative nodule, low-grade dysplastic nodule, high-grade dysplastic nodule, small HCC, and large HCC. State-of-the-art magnetic resonance (MR) imaging facilitates detection and characterization in most cases of hepatic nodules. State-of-the-art MR imaging includes single-shot fast spin-echo imaging, in-phase and opposed-phase T1-weighted gradient-echo imaging, T2-weighted fast spin-echo imaging with fat saturation, and two-dimensional or three-dimensional dynamic multiphase contrast material-enhanced imaging

Proposed Hybrid Cryptosystems Based on Modifications of Playfair Cipher and RSA Cryptosystem

Cipher security is becoming an important step when transmitting important information through networks. The algorithms of cryptography play major roles in providing security and avoiding hacker attacks. In this work two hybrid cryptosystems have been proposed, that combine a modification of the symmetric cryptosystem Playfair cipher called the modified Playfair cipher and two modifications of the asymmetric cryptosystem RSA called the square of RSA technique and the square RSA with Chinese remainder theorem technique. The proposed hybrid cryptosystems have two layers of encryption and decryption. In the first layer the plaintext is encrypted using modified Playfair to get the cipher text, this cipher text will be encrypted using squared RSA to get the final cipher text. This algorithm achieved higher security to data but suffers from a long computational time. So Chinese remainder theorem has been used in the second hybrid cryptosystem to obtain less encryption and decryption time. The simulation results indicated that using the modified Playfair with the proposed square RSA has improved security. Moreover, using the Chinese remainder theorem achieved less encryption and decryption time in comparison to our first proposed and the standard algorithms

Performance evaluation of IEC 61850 MMS messages under cybersecurity considerations

IEC 62351-4 standard is published to address cybersecurity vulnerabilities of IEC 61850 Manufacturing Message Specification (MMS) messages. This standard includes a set of cipher suites that are recommended for securing MMS messages. However, these are only a set of recommendations. There is no work in the literature that implements them on an IEC 61850 MMS message and reports the performances. In order to fill this importance knowledge gap, this short communication reports results of implementing cipher suites recommended by IEC 62351-4 on IEC 61850 messages. In addition to implementation details, real message exchanges are demonstrated with lab experiments. Finally, changing certificate and message sizes are reported. The results show that cipher suite selection is critical as some suites have 29.67 % smaller certificate size than others. The novelty of this short communication is showing details of IEC 62351 application and relevant changes on message sizes and structures of IEC 61850 MMS messages. There is no similar work or publication showing such procedures and results

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model