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Cesspits are the means for each house to dispose of wastewater in the Bani Kinanah District (BKD) of Jordan, which creates severe environmental complications. This research aimed to find a suitable site for a wastewater treatment plant (WWTP) in BKD.

Geographic Information System (GIS)-based multicriteria decision analysis (MCDA) was used for an optimal site selection for a sewage treatment plant. Several datasets were obtained to prepare the maps of the criteria influencing the choice of the most suitable site for the WWTP. The analytic hierarchy process was used to apply the weights for each factor.

Five classes of suitability were generated: 0.23% very high suitability, 8.49% high suitability, 47.12% moderate suitability, 37.67% low suitability and 6.49% very low suitability. According to Analytical Hierarchy Process (AHP) results, the elevations, slope and groundwater depth have high importance; where their weights 21%, 19% and 17%, respectively. The most suitable site for establishing a WWTP was found in the northern part of the study area, where it is characterized by relatively low elevations (−90 to −93 m), low slope (0–2.5 %), distance from groundwater level (47–82 m) and the space is sufficient for building the plant (25328 m², 8861 m² and 8586 m²).

This research is limited by the availability of data.

The research is invaluable for decision makers involved in urban planning.

Wastewater treatment plants are essential for communities with limited resources such as Jordan. It has also profound impacts on the surrounding environment.

From the present study, it can be concluded that GIS is essential in urban utility establishment, like urban domestic wastewater treatment site selection. Although the study area has adequate potential areas for establishing WWTP, further assessment of flood vulnerability, wastewater amount quantification, population growth and urban expansion must be seriously considered before implementation.

Many treatment methods for intra-articular fractures of the proximal interphalangeal (PIP) joint are described in the literature without a consensus on the most effective approach. The purpose of this study was to investigate the methods of treatment of PIP joint fractures being used by trauma surgeons in the Republic of Ireland currently and the timing of referral to therapy.

A cross-sectional descriptive study methodology was used to survey trauma surgeons, occupational therapists and physiotherapists in Ireland. An online platform was used. A total of 21 surveys were returned by surgeons and 37 by therapists. Descriptive statistical analysis was used to present the results.

Buddy strapping was reported as the primary treatment method for stable PIP joint fractures. All levels of fracture severity were reported to be treated using traction constructs, which include static and dynamic fixation and orthoses. Unstable fractures were managed using open reduction with internal fixation by 50 per cent of surgeons. Early timing of referral to therapy is reported by more surgeons than therapists. The majority of therapists indicated that they did not have the resources to see patients at the optimal time.

To the authors’ knowledge, this study provides the first description of the management of PIP joint fractures across the Irish health service. The findings of this study suggest that additional therapy resources are required within the health service executive to facilitate the desired early referral to therapy and to enable service development for this category of hand fractures.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

The purpose of this paper is to analyse the association between drug consumption and unemployment. This paper also studies the differential association between these variables in both the pre- and current-crisis periods. The results are compared in an attempt to verify that the population of users is more vulnerable in terms of how likely they are to get and hold down a job in the labour market.

Matching methods and microdata from the Survey on Alcohol and Drugs in Spain, EDADES are used. The use of these methods on the estimates carried out prove to be particularly effective in reducing treatment-selection bias. The authors’ interest is also to analyse the differential association between the interest variables in both the pre- and current-crisis periods. For this purpose, the authors also use the differences-in-differences (DID) estimation method between the two periods to check if the impact of drug use on unemployment depends on the economic context. The estimations are compared in an attempt to verify that the population of users is less likely to attain and hold down a job in the labour market than non-drug users.

The results obtained in the current study are consistent with the hypothesis that drug use decreases an individual’s capacity and availability when he or she is trying to enter the labour market. In both 2007 and 2013, drug users were more likely to be unemployed, regardless of the type of drug. Differences in the probability of being unemployed intensify during an economic crisis. In light of these results, it is possible to conclude that the negative effect of drug consumption on an individual’s employability is increased during periods of economic recession.

The study presented here has some limitations. Firstly, cross-sectional data were used to examine the causal relationship between consumption and employment. In this sense, the results are susceptible to bias. The unavailability of longitudinal data on the same individual made it impossible for the researchers to consider periods of abstinence, the duration of periods of consumption and how this consumption affected an individual’s productivity and his or her working situation. Another limitation is that certain relevant unemployment variables may have been omitted. Among the variables that affect an individual’s labour participation is the existence of sources of income as an alternative to market salaries. With state subsidies, income from illegal activities and money sent by family or friends, an individual may decide not to work. This problem could be mitigated if omitted variables operate in a similar way throughout both of the periods examined.

Given the results obtained in this paper, the authors believe that public policy conclusions should be mainly concerned with the importance of implementing proactive employment policies, along with family support programmes and a greater role for primary care among the people with the highest risks of exclusion. Health treatment should go jointly with measures that make it easier for individuals to enter the workforce. These steps would only be possible with an improved level of education and more complete professional profiles, to increase motivation when individuals seek employment.

This study could make various contributions to the existing body of evidence. In the authors’ knowledge, this is the first attempt to document the effect of the economic crisis on the employability of the drug-using population in contrast with the general population. Moreover, a methodology is presented that provides an alternative to those used in earlier studies, in terms of reducing treatment-selection bias. At the same time, the use of a DID estimation method between pre- and current-crisis periods allow us to check if the impact of drugs consumption on unemployment depends on the economic context.

The purpose of this paper is to evaluate a recent training programme for graduates, implemented in Italy and entitled Work Experience Laureati and Laureate, i.e. Work Experience for Graduates. The aim of the programme was to increase the career prospects of unemployed graduates in the region of Umbria.

The authors rely on administrative data and matching methods to evaluate the effectiveness of the intervention in terms of employability of participants.

The results show that participants are more likely to be employed and to sign an apprenticeship contract within the region boundaries. The authors also find substantial differences in employability and type of contract by gender, with men having a higher probability of finding a job (permanent contract and apprenticeship). The authors show that this may be explained by the different choices in terms of field of study, with males being more prone to enrol in scientific areas and females in the humanities.

It is an intervention implemented in one Italian region.

This is one of the few studies that analyses the effectiveness of active labour market policies targeting unemployed graduates, especially in the Italian context. The authors rely on different administrative data sources that allow them to evaluate the effectiveness of the programme.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Female smallholder farmers in low-income countries face barriers to accessing capital and commodity markets. While agricultural cooperatives provide services that contribute to the income and productivity of small-scale producers, evidence of cooperatives' social and economic empowerment of female smallholders remains limited. We apply Sen's capability approach to female entrepreneurs' socioeconomic empowerment to examine whether women's participation in a coffee and microfinance cooperative from rural western Uganda benefits their social and economic position within their household. First, we study the relationship between women's cooperative participation and their household coffee sales and savings. Second, we investigate the link between women's cooperative participation and their intra-household decision-making and whether the inclusion of the husband in his wife's cooperative strengthens or lowers women's decision-making power.

We carry out a case study of a hybrid coffee and microfinance cooperative that promotes social innovation through the integration and empowerment of female smallholders in rural Uganda. Using a cross-sectional survey of 411 married female cooperative members from 26 randomly selected self-help groups of Bukonzo Joint Cooperative and 196 female non-members from the identical area, employing propensity score matching, this paper investigates the benefits of women's participation in a coffee and microfinance cooperative in the Rwenzori Mountains of western Uganda. We present and discuss the results of our case study within an extensive literature on the role of institutions in collective action for women's empowerment.

Our findings provide new empirical evidence on female smallholders' participation in mixed cooperatives. Our results indicate that women's participation in microfinance-producer cooperatives appears to be a conditional blessing: even though membership is linked to increased women's intra-household decision-making and raised household savings and income from coffee sales, a wife with a husband in the same cooperative self-help group is associated with diminished women's household decision-making power.

The focus of this study is on female coffee smallholders in an agricultural cooperative in rural western Uganda. In particular, we focus on a case study of one major coffee cooperative. Our cross-sectional survey does not allow us to infer causal interpretations. Also, the survey does not include variables that allow us to measure other dimensions of women's empowerment beyond decision-making over household expenditures and women's financial performance related to savings and income from coffee cultivation.

Our empirical results indicate that female smallholders' cooperative membership is associated with higher incomes and coffee sales. However, husband co-participation in their wives' cooperative group diminishes wives' decision-making, which suggests that including husbands and other family members in the same cooperative group may not be perceived as an attractive route to empowerment for female smallholders. For these reasons, an intervention that encourages the cooperation of both spouses and that is sensitive to context-specific gender inequalities, may be more successful at stimulating social change toward household gender equality than interventions that focus on women's autonomous spheres only.

While the literature thus far has focused on microfinance's potential for women's empowerment, evidence on agricultural cooperatives' affecting women's social and economic position is limited. First, our findings provide novel empirical evidence on the empowering effects of women's participation in a self-help group-based coffee cooperative in rural Uganda. Second, our data allows us to explore the role of husbands' participation in their wives' cooperative and SGH. We embed our hypotheses and empirical results in a rich discussion of female entrepreneurship, microfinance and cooperative literature.

Contract farming (CF) is seen as a tool for creating new market opportunities hence increasing incomes for smallholder farmers. Critics, however, argue that CF is likely to pass risks to small scale farmers, thus favouring large scale farmers at the expense of smallholder farmers. The purpose of this paper is to examine the effect of CF on smallholder farmers’ income using a case study of avocado farmers in Kandara district in Kenya.

The study uses data collected from 100 smallholder avocado farmers in Kandara district in Kenya and employs an instrumental variable model (Probit-2SLS) to control for endogeneity in participation in the contract and examine the effect of CF on household, farm and avocado income.

The results indicate that participation in CF is not sufficient to improve household, farm and avocado income. Question remains regarding efficient implementation of CF arrangements to promote spill over effects on other household enterprises.

The research was carried out using farmers in Kandara district in Kenya as a case study, findings might therefore not reflect the status of CF in all countries.

The paper contributes to the growing debate on the effect of value chain upgrading strategies such as contracting on smallholder farmers’ welfare. The form of contracting studied in this paper differs from the standard contracts in that the key stakeholders (producers) are loosely enjoined in the contract through officials of their groups.

This paper aims to examine the effects on firms' outcomes of a new government regulation on the private security industry that aimed to enhance the selection and training processes for armed-private security officers.

By using human capital theory and using a data set built from various public sources, this study analyzes the effects of a new regulation implemented in 2013–2014 in Brazil mandating psychological assessments for hiring private security armed officers. Firm-level data and a Difference-in-Differences (DiD) identification strategy are used to investigate the effects on turnover and human capital outcomes.

The study identifies substantial changes resulting from the new government regulation in private security firms. While it has led to increased turnover rates, the regulation has also facilitated firms in enhancing the human capital composition of their workforce by enabling the recruitment of more experienced personnel.

This research informs to current debates on the effects of policy interventions on firm's outcomes by showing how regulations aimed to improve the configuration of human capital can generate win-win situations for both firms and citizens, despite the short-term trade-offs between higher turnover rates and improved human capital outcomes.

Refining selection and training processes can enhance the workforce in private security firms by replacing less capable professionals with more experienced ones. Insights from this study offer guidance to policymakers and industry practitioners in shaping effective business and public policies.

This study underscores the role of training and psychological assessments in enhancing the composition of human capital in the private security industry.

By highlighting the role of policy interventions in establishing barriers to unskilled workers engaging in hazardous activities, this study contributes to the burgeoning literature in strategic management on the interaction between policy interventions and firm outcomes.