Search Results (4)

Vegetable consumption in childhood remains below recommendations worldwide, particularly in disadvantaged socioeconomic groups. Building on prior work showing no socioeconomic status (SES) differences in children’s liking of familiar vegetables, this study examined whether their sensory and emotional descriptions vary by SES and how these relate to liking beyond hedonic ratings. A total of 363 Chilean fourth graders (9–10 years) from five SES groups evaluated eight vegetables at school. For each sample, children rated overall liking (7-point facial hedonic scale) and completed two CATA (Check-All-That-Apply) tasks: a child-derived sensory list (13 terms) and a validated emoji-based emotion list (33 items). Data were analyzed using Cochran’s Q tests, correspondence analyses, and mean-impact analyses. The use and diversity of sensory and emotional descriptors differed significantly between socioeconomic groups (p < 0.05): children from higher SES levels employed a broader and more differentiated vocabulary, while those from lower SES backgrounds used fewer significant terms. Across the sample, juicy, fresh, and mild flavors increased liking, whereas strong aroma decreased it (p < 0.05); positive emojis increased liking, whereas negative and neutral ones had no effect. These findings reveal that perceptual and affective representations are socially patterned, underscoring the need to foster sensory–affective literacy in lower-SES contexts. Full article

Common practice in agile delivery planning is based on user requirements-related artifacts. However, an aspect of business process alignment to software product functions comes into focus in the phase of inception of enterprise-aware disciplined agile software projects. This research proposes a method for mapping business process model elements to sets of semantically coupled and prioritized software functions to obtain ordered software product backlog, i.e., work items list. These software functions are derived from primitive business processes and software functional patterns. The mapping table enables assignment of primitive business processes to categorized software functions. Derived and prioritized software functions are related to a work item list pattern according to selected technology implementation. This way, a prioritized work items list is formulated, which enables development iteration planning. This method could be useful in software functional design alternatives comparison, change management, multi-project integration of software modules to support business processes in information systems, etc. Feasibility of the proposed method has been demonstrated with a case study, related to the development of a billing and reporting software utilized in a private hospital. This case study shows usability of the proposed method in the case of two related development projects that enable software functionality enhancement. Full article

Background: Health-related quality of life is rapidly becoming recognized as an important indicator of how a disease affects patient lives and for evaluating the quality of care, especially for chronic conditions such as chronic kidney disease (CKD). Objectives: This study is an attempt to assess the quality of life in patients with chronic kidney disease at MMIMSR and also identify characteristics that may be associated with their worsening quality of life. Materials and Methods: This cross-sectional investigation was conducted at the in-patient department (IPD) of the MMIMSR hospital. This study included 105 CKD patients and used a systematic random sampling method for quantitative analysis. This study utilized a 36-item short-form SF-36 (v1.3) questionnaire to assess HRQoL in CKD patients. Descriptive statistics were employed at the baseline. Chi square and ANOVA were used to draw comparisons between two groups or more than two groups, respectively. Logistic regression analysis was utilized to identify the potential QoL determinants. A p value of 0.05 or lower was used to determine statistical significance. Results: Among a total of 105 participants, the mean (±standard deviation) age was found to be 54.53 ± 13.47 years; 48 were male patients, and 57 were female patients. Diabetes Mellitus (61.9%), hypertension (56.2%), chronic glomerulonephritis (7.6%), chronic pyelonephritis (6.7%), and polycystic kidney disease (5.7%) were identified to be the most frequent disorders associated with CKD. The current study also demonstrated that the HRQoL score domains such as symptom problem list, the effect of kidney disease, and the burden of kidney disease decline significantly and progressively as the patient advances into higher stages of CKD (p = 0.005). A similar pattern was observed in work status, sleep, and general health (p < 0.005). Additionally, a statistically significant difference was noted for cognitive function, quality of social interaction, overall health, dialysis staff encouragement, patient satisfaction, social support, physical functioning, role of physical health, pain, emotional well-being, role of emotional health, social functioning, and energy fatigue (p < 0.005). The mean difference for PCS and MCS based on CKD stages was found to be statistically significant (p < 0.005). The PCS and MCS showed a positive correlation with GFR (r = 0.521), and Hb (r = 0.378), GFR (r = 0.836), and Hb (r = 0.488), respectively. Conclusions: The findings of this study demonstrated that a significant decrease in HRQoL was observed among CKD patients, with a progressive deterioration of HRQoL dimensions as the patient advances to end-stage renal disease. This study also revealed that CKD imposes various restrictions on patients’ day-to-day lives, particularly in terms of their physical and mental functioning, even in the initial stages of the disease. Full article

A ubiquitous challenge throughout all areas of data mining, particularly in the mining of frequent patterns in large databases, is centered on the necessity to reduce the time and space required to perform the search. The extent of this reduction proportionally facilitates the ability to identify patterns of interest. High utility sequential pattern mining (HUSPM) seeks to identify frequent patterns that are (1) sequential in nature and (2) hold a significant magnitude of utility in a sequence database, by considering the aspect of item value or importance. While traditional sequential pattern mining relies on the downward closure property to significantly reduce the required search space, with HUSPM, this property does not hold. To address this drawback, an approach is proposed that establishes a tight upper bound on the utility of future candidate sequential patterns by maintaining a list of items that are deemed potential candidates for concatenation. Such candidates are provably the only items that are ever needed for any extension of a given sequential pattern or its descendants in the search tree. This list is then exploited to significantly further tighten the upper bound on the utilities of descendent patterns. An extension of this work is then proposed that significantly reduces the computational cost of updating database utilities each time a candidate item is removed from the list, resulting in a massive reduction in the number of candidate sequential patterns that need to be generated in the search. Sequential pattern mining methods implementing these new techniques for bound reduction and further candidate list reduction are demonstrated via the introduction of the CRUSP and CRUSPPivot algorithms, respectively. Validation of the techniques was conducted on six public datasets. Tests show that use of the CRUSP algorithm results in a significant reduction in the overall number of candidate sequential patterns that need to be considered, and subsequently a significant reduction in run time, when compared to the current state of the art in bounding techniques. When employing the CRUSPPivot algorithm, the further reduction in the size of the search space was found to be dramatic, with the reduction in run time found to be dramatic to moderate, depending on the dataset. Demonstrating the practical significance of the work, experiments showed that time required for one particularly complex dataset was reduced from many hours to less than one minute. Full article