Despite noticeable research interest, the labor-intensive Readymade Garments (RMG) industry has rarely been studied from the perspective of workers’ productivity. Additionally, previous studies already generalized that rewards and organizational commitment lead to employee productivity. However, extant research focused on the RMG industry of Bangladesh, which consists of a different socio-cultural, economic, and political environment, as well as profusion dependency on unskilled labor with an abundance supply of it, hardly considered job satisfaction as a factor that may affect the dynamics of compensations or rewards, commitment, and employee productivity. To address this research gap, this study analyzes the spillover effect of compensation, organizational commitment, and job satisfaction on work productivity in Bangladesh’s readymade garments (RMG) industry. Besides, it delves into the analysis of job satisfaction as a mediator among these relationships. We examined the proposed model by analysing cross-sectional survey data from 475 respondents using the partial least squares-structural equation model in Smart PLS 4.0. The findings show that higher compensation and organizational commitment levels lead to higher levels of job satisfaction, leading to greater productivity. This research also discovered that job satisfaction is a mediator between compensation and productivity and commitment and productivity, respectively. Results further show that increased organizational commitment and competitive wages are the two keyways to boost job satisfaction and productivity in the RMG industry. Relying on the findings, this study outlines pathways for organizational policymakers to improve employee productivity in the labor-intensive industry in developing countries.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

The PPP scholarly work has effectively explored the material values attached to PPPs such as efficiency of services, value for money and productivity, but little attention has been paid to procedural public values. This paper aims to address this gap by exploring how Enfidha Airport in Tunisia failed to achieve both financial and procedural values that were expected from delivering the airport via the PPP route, and what coping strategies the public and private sectors deployed to ameliorate any resultant value conflicts. Based on the analysis of Enfidha Airport, it is argued that PPP projects are likely to fail to deliver financial and procedural values when the broader institutional context is not supportive of PPP arrangements, and when political and security risks are not adequately counted for during the bidding process.

The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

The study intends to identify the existing implementation bottlenecks that hamper the effectiveness of the Ethiopian forest policy and laws in regional states by focusing on the Oromia Regional State. It attempts to address the question, “What are the challenges for the effective implementation of the federal forest policy and law in Ethiopia in general and Oromia Regional State in particular?”. The study followed a qualitative research approach, and the relevant data was collected through in-depth interviews from 11 leaders and experts of the policy, who were purposively selected. Furthermore, relevant documents such as the constitutions, forest policies and laws, and government documents were carefully reviewed. Based on this, the study found that there is the dichotomy between the provision of the constitution regarding the forest policy and lawmaking and the constitutional amendment on one hand and the push for genuine decentralization in the Ethiopian federal state on the other. To elaborate, the constitution is rigid for amendment, and it has given the power of forest policy and lawmaking to the federal government. On the other hand, the quest for genuine decentralization requires these powers to be devolved to the regional states. As the constitution is rigid, this may continue to be the major future challenge of the forest policy and lawmaking of the state. This demonstrates a conflict of interests between the two layers of governments, i.e., the federal and regional (Oromia Regional State) governments. Respecting and practicing the constitution may be the immediate solution to this pressing problem.