Daily Basis Research Articles

SARI: Shared Autonomy across Repeated Interaction

Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy . Within shared autonomy, both the human and robot maintain control over the robot’s motion: as the robot becomes confident it understands what the human wants, it intervenes to automate the task. But how does the robot know these tasks in the first place? State-of-the-art approaches to shared autonomy often rely on prior knowledge. For instance, the robot may need to know the human’s potential goals beforehand. During long-term interaction these methods will inevitably break down—sooner or later the human will attempt to perform a task that the robot does not expect. Accordingly, in this article we formulate an alternate approach to shared autonomy that learns assistance from scratch. Our insight is that operators repeat important tasks on a daily basis (e.g., opening the fridge, making coffee). Instead of relying on prior knowledge, we therefore take advantage of these repeated interactions to learn assistive policies. We introduce SARI, an algorithm that recognizes the human’s task, replicates similar demonstrations, and returns control when unsure. We then combine learning with control to demonstrate that the error of our approach is uniformly ultimately bounded. We perform simulations to support this error bound, compare our approach to imitation learning baselines, and explore its capacity to assist for an increasing number of tasks. Finally, we conduct three user studies with industry-standard methods and shared autonomy baselines, including a pilot test with a disabled user. Our results indicate that learning shared autonomy across repeated interactions matches existing approaches for known tasks and outperforms baselines on new tasks. See videos of our user studies here: https://youtu.be/3vE4omSvLvc .

Use of Mesenchymal Stem Cells in Experimental Ovarian Damage.

Bisphenol-A (BPA) has a well-proven deleterious effect on the hypothalamicpituitary- gonadal axis. The current study investigated the therapeutic potentials of mesenchymal stem cells (MSCs) in a murine model of BPA-induced ovarian damage. Fifty adult female rats were divided into: Group 1; control group, Group IIa, IIb: rats were given oral gavage of BPA (25 and 50 mg/Kg body weight respectively) on a daily basis for 15 days, and Group IIIa, IIIb; rats were intravenously treated with of MSCs (106 cells) after receiving the last dose of BPA as in group II. Plasma and ovarian tissue levels of Malondialdehyde (MDA) and gonadal axis hormones were assessed. Apoptosis was evaluated by TUNNEL assay and by apoptosis markers (FAS, FASL, Caspase 3, SLTM). A histological examination of ovarian tissue was also conducted. BPA resulted in a significant elevation in plasma levels of LH, FSH, and ovarian tissue levels of MDA and a significant decrease in estradiol and progesterone. All genetic and protein markers of apoptosis were elevated in BPA treated group with decreased oestrogen receptor expression in the ovarian tissue. Increased apoptotic cells were confirmed by TUNEL assay. A high dose of BPA was able to increase the number of atretic follicles in the ovarian tissue whereas the numbers of primordial, primary, secondary and Graafian follicles were decreased. All the laboratory and histological abnormalities were ameliorated by treatment with MSCs. The antioxidant and anti-apoptotic effects of MSCs could possibly explain the ability of this therapeutic modality to ameliorate BPA-induced-ovarian damage.

A Condition Knowledge Representation and Feedback Learning Framework for Dynamic Optimization of Integrated Energy Systems.

An optimal energy scheduling strategy for integrated energy systems (IESs) can effectively improve the energy utilization efficiency and reduce carbon emissions. Due to the large-scale state space of IES caused by uncertain factors, it would be beneficial for the model training process to formulate a reasonable state-space representation. Thus, a condition knowledge representation and feedback learning framework based on contrastive reinforcement learning is designed in this study. Considering that different state conditions would bring inconsistent daily economic costs, a dynamic optimization model based on deterministic deep policy gradient is established, so that the condition samples can be partitioned according to the preoptimized daily costs. In order to represent the overall conditions on a daily basis and constrain the uncertain states in the IES environment, the state-space representation is constructed by a contrastive network considering the time dependence of variables. A Monte-Carlo policy gradient-based learning architecture is further proposed to optimize the condition partition and improve the policy learning performance. To verify the effectiveness of the proposed method, typical load operation scenarios of an IES are used in our simulations. The human experience strategies and state-of-the-art approaches are selected for comparisons. The results validate the advantages of the proposed approach in terms of cost effectiveness and ability to adapt in uncertain environments.

Fermentation Kinetics, Potability, and Sensory Compounds of Joubidwi: An Ethnic Alcoholic Beverage

Background: Fermentation is a metabolic pathway of the breakdown of carbohydrate molecules like glucose, fructose to ethanol and various volatile and non-volatile congeners. The production of alcoholic beverages from foodstuffs like rice, cereals, fruits, maize etc., is an age-old traditional protocol. In this study, we adapted the traditional protocol of preparation of the ethnic beverage Joubidwi, with a fermentation time of 6 days, i.e., aliquots from the fermented mash (cooked rice) were collected on a daily basis from day 1 to day 6. Objectives: This study aimed to get insight into fermentation kinetics, ethanol content, carbohydrate content, antioxidant activities, bitterness, protein content, pH, colour, and turbidity of the ethnic alcoholic beverage Joubidwi and to identify the sensory compounds and polyphenol molecules. Method: UV-Visible spectroscopic methods, GC-FID and GC-MS analytics methods were used for the estimation and identification of carbohydrates, proteins, ethanol contents, and sensory compounds, respectively. Reverse phase HPLC was adapted for the identification of polyphenol molecules in Joubidwi. Results: A pseudo-first-order fermentation kinetics was observed from day 1 to day 6 fermentation of Joubidwi. Apart from ethanol content (60.48 ± 1.36 mg/mL after 72 h fermentation to 78.80 ± 1.48 mg/mL after 144 h fermentation), the beverage had nutritional values and good antioxidant properties with 22.38 to 35.11% Radical Scavenging Effect (RSE) from 1 to 6th day of fermented Joubidwi. The beverage also contained 12 sensory compounds. Gallic acid was identified as polyphenol in Joubidwi samples. Conclusion: Joubidwi is an alcoholic beverage prepared by Bodos using rice and certain herbs through a traditional fermentation process. The fermentation process follows pseudo-first-order kinetics. The beverage is potable.

Associations between daily food insecurity and parent and child well-being.

Food insecurity poses a serious threat to children's development, but the mechanisms through which food insecurity undermines child development are far less clear. Specifically, food insecurity may influence children through its effect on parents' psychological well-being and parent-child interactions as a result, but past research on the role of parents is correlational and undermined by omitted variable bias. Using a partially rural, low-income sample of parents living in Pennsylvania (N = 272, 90% mother, Mage = 35) and their school-aged children (ages 4-11, 50% female) alongside daily measures of parent-reported food insecurity and parent and child mood and behavior, we examine how daily changes in food insecurity predict daily changes in parent and child well-being, and the extent to which food insecurity operates through parents to affect children. This method not only explores families' daily, lived experiences of food insecurity, but improves upon the methodological issues undermining past research. Findings indicate that food insecurity influences parent and child well-being on a daily basis, but that associations are stronger and sustain longer for parents than children. Further, parent mood and behavior partially explain the association between daily food insecurity and child mood and behavior, but food insecurity is also independently associated with child well-being. This study is the first to examine daily associations between food insecurity and parent and child well-being. Its implications for food assistance programs, policies, and the future of food insecurity research are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Performance analysis of photovoltaic panel using machine learning method

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.

Analysing the effectiveness of Just-A-Minute Optometry Clinical Pearls: a micro-learning tool in continuing optometry education

ABSTRACT Clinical relevance Just-A-Minute Clinical Pearls as a microlearning concept may be beneficial in enhancing optometry and ophthalmology practice globally. Background Medical education often witnesses a gap in effectively translating the learnings into clinical practice, pointing to the complex and traditional teaching methods as hindrances. The present work studied the usefulness and acceptability of Just-A-Minute Optometry Clinical Pearls, a micro-learning tool, among optometrists and ophthalmologists. Methods Just-A-Minute Optometry clinical pearls were developed by the optometry team of LV Prasad Eye Institute and shared (via email) among optometrists and ophthalmologists on a daily basis between June 2021 to May 2022. In the middle of the project, the recipients were invited to participate in an online survey. The variables studied included frequency of checking clinical pearls, simplicity of their content, grasping speed, most used subspeciality pearls, knowledge gain and retention, practice applicability, overall learning experience, and the likelihood of recommending it to a friend. Results Among 150 respondents, 103 (68.7%) were ophthalmologists, and 46 (30.7%) were optometrists. The majority were from private (n = 64, 42.7%) and institutional (n = 48, 32%) practices, with 102 (68.4%) having more than five years and 21 (14%) having 2–5 years of experience. About 115 (77%) respondents checked clinical pearls every day, 147 (99%) found the format easy, and 131 (88%) could grasp the content within one minute. They felt that JAM-OCP ‘always’ enhanced clinical knowledge (n = 108, 72%) and clinical applicability (n = 82, 55%) and helped in knowledge retention (n = 123, 84%). The responses to the clinical application significantly varied (Chi-square tests) among subgroups of education qualification (p < 0.001) and practice types (p < 0.03). Conclusions The Just-A-Minute Optometry Clinical Pearls were beneficial to optometrists and ophthalmologists in their practices. This, as a tele-education tool, supports continuing optometry education across the globe.

Spatial and Temporal Distribution of Soil Moisture in Maize Using Frequency Domain Reflectometry Sensor

Field experiment was conducted during kharif 2018, rabi 2018-19 and kharif 2019 at Department of Soil Water Engineering, Dr. NTR College of Agricultural Engineering, Bapatla, Andhra Pradesh to study the spatial and temporal distribution of soil moisture in maize using FDR sensor.The experiment was laid out in split plot design consisting of three irrigation levels (0.6 ETc (I1), 0.8 ETc (I2) and 1.0 ETc (I3)) and four nitrogen levels consisting of fertigation levels (80% RDN (N1), 100% RDN (N2), 120% RDN (N3) and Manual application (N4) on sandy clay loam soil. Water requirement of the maize crop was calculated on the daily basis by using CROPWAT 8.0. Frequency Domain Reflectometry (FDR) Sensor was used to measure soil moisture data by installing 34 access tubes up to a depth of 100 cm with an interval of 10 cm from the soil surface and measured soil water content during crop period. Soil moisture was evaluated for 2.0 lph emitter discharges at 3h after irrigation, 6h after irrigation, 24h after irrigation at emitter, 15 cm from emitter, 30 cm from emitter and 45 cm from emitter for soil moisture in the soil for arriving the best irrigation strategy. The soil water distribution under all drip irrigation treatments indicated that it was relatively high near the dripper and decreased as the distance from the dripper point increased.

Exploring Design Strategies for Cultivating Sustainability and Enhancing Brand Image in Personal Care Product Brands

Through strategically implementing sustainable practices, businesses can enhance their public image and incentivize consumers to purchase environmentally friendly products. In recent years, many enterprises have actively pursued corporate social responsibility, striving to achieve profitability, fairness, and societal benefits simultaneously, thus creating a beneficial cycle for both businesses and the environment. The COVID-19 pandemic has altered brand perceptions and consumer habits, impacting not only packaging materials, product life cycles, and corporate social responsibility but also significantly impacted sustainability. Personal care products are utilized by individuals on a daily basis, emphasizing the need to consider various indicators in designing brand image strategies to ensure sustainable development. This study focuses on indicators for sustainable brand image design strategies within the personal care product indicators, employing a modified Delphi method to establish these indicators. The relative weights of these indicators were determined using the Analytic Hierarchy Process. The research findings underscore that conveying brand ideals to consumers, committing to brand values, enhancing brand visibility, and creating value are considered the most crucial indicators in the design of sustainable branding strategies for personal care products. These results provide essential insights for researchers and brand developers to formulate corresponding strategies, thereby achieving sustainable brand development and enhancing competitiveness.

Disposal of solid waste from civil construction: a screening proposal for a suitability system and case study in Nepomuceno, Minas Gerais

ABSTRACT Most Brazilian municipalities do not have regulated areas for solid waste disposal in civil construction. Usually, residues are disposed of vacant lots and dumps, posing risks to the population health and the environment. Soils are the primary means for the disposal or recycling of waste, highlighting the importance of well-characterized soils and their respective landscape. This study aimed to establish a land suitability system for solid residues in civil construction and apply such information in a case study in Southeastern Brazil. An unprecedented digital soil map with a resolution of 30 m was created using the random forest classifier algorithm and soil field prospection information. A guide listing favorable soil-landscape attributes that most prevent soil erosion, water bodies or water table contamination was elaborated and discussed. Thus, such information was linked through a suitability system to classify areas with potential for receiving waste on a daily volume basis as follows: large size: &gt;500 m³ day -1 , medium size: &gt;100 m³ day -1 and &lt;300 m³ day -1 , and small size &lt;100 m³ day -1 . Topography and soil depth were the most limiting factors of the areas in the case study. The proposed attributes as criteria for the suitability system complement the current state legislation. A total of 236 ha closer to the urban perimeter connected by roads in good condition were classified as suitable for managing medium- and small-scale daily volume, whose destination might reduce transportation and installation costs in the study area.

EsophaCap Sponge Cytology Screening for Esophageal Squamous Cell Dysplasia and Carcinoma is Feasible in a High-Risk Area in Western India

Objective: Conventional endoscopy remains the gold standard for detecting esophageal squamous cell carcinoma (ESCC) despite its high cost and need for expertise. In resource-poor regions, a less labor-intensive yet accurate screening tool is needed. The purpose of this study is to assess feasibility of establishing a non-endoscopic screening program in rural India and to test the diagnostic accuracy of the EsophaCap swallowable sponge in detecting ESCC in a high-risk patient population. Methods: A prospective cohort study was conducted between 2017 and 2019, in which subjects with risk factors for ESCC (tobacco smoking or chewing, betel nut/leaf, alcohol and hot beverage consumption) were approached during upper endoscopy visits at a clinic in Western India. After obtaining EsophaCap sponge cytology samples, random endoscopic biopsies were obtained at 20 and 30 cm from the incisors. Histologic diagnoses were confirmed and select biopsy samples were sequenced for genomic aberrations. Visual Analog Scale (VAS) scores were used to assess patient experience of sponge swallowing (range 1-5, “very comfortable” to “very uncomfortable”). Results: In our cohort of 178 patients, 157 (88%) were males. Mean age was 52 ± 12 years. Sixty-eight (38%) patients were current cigarette or bidi smokers; 132 (74%) patients were daily tobacco chewers, and 83 (47%) patients chewed betel nuts/leaves on a daily basis. Forty-six (26%) patients were daily alcohol users and 151 (85%) patients drank ≥3 hot beverages per day. The median number of risk factors per patient was 3. The first-time swallow rate of the encapsulated sponge was successful in 190/200 (95%) patients. Median VAS score was 2 (“comfortable”). EsophaCap cytology revealed 6 (3%) patients with atypical squamous cells of unknown significance (ASCUS) and 3 (1%) patients with dysplasia. Based on the endoscopic biopsies, 6 (3%) patients had ESCC, 4 (2%) patients had lesions with squamous dysplasia, and 63 (35%) patients had esophageal leukoplakia. Four patients classified as ASCUS pathology via EsophaCap were normal, benign, or leukoplakia via endoscopy. EsophaCap’s sensitivity and specificity for detecting dysplasia or ESCC by histology was 30% and 97%, respectively. Conclusions: Establishing a non-endoscopic screening program in a high-risk area with language barriers and low medical literacy is very safe and feasible. EsophaCap may help identify ASCUS patients in need of serial endoscopic monitoring. Further studies of combined sponge cytology with immunohistochemistry studies are necessary to improve accuracy of ESCC screening.

Warm‐adapted sponges resist thermal stress by reallocating carbon and nitrogen resources from cell turnover to somatic growth

AbstractOcean warming will affect the functioning of coral reef ecosystems with unknown cascading effects. Any perturbation in the ability of sponges to recycle the dissolved organic matter released by primary producers and make it available to higher trophic levels, might have unknown consequences for the reef trophic chain. Biogeochemical processes were measured in the sponge Rhabdastrella globostellata from the semi‐enclosed lagoon of Bouraké, where temperatures reach 33.8°C and fluctuates by 6.5°C on a daily basis, and from a control reef (28°C). Using 13C‐ and 15N‐labeled coral mucus, we experimentally investigated to what extent high temperature affected the carbon (C) and nitrogen (N) resources allocation in the sponge tissue and detritus. Sponges from Bouraké maintained at 32°C incorporated more 13C‐ and 15N‐labeled coral mucus in the tissue and showed less detritus release when compared with sponges maintained at 28°C. In contrast, at 32°C control sponges showed lower 13C‐ and 15N‐labeled coral mucus incorporation in tissue and higher release of detritus. Our results suggest that sponges adapted to extreme temperatures of Bouraké were able to reallocate C and N resources from cell turnover to somatic growth and reduce tissue damage. In contrast, non‐adapted sponges at the control reef lack this mechanism and underwent tissue disintegration, highlighting the lethal effect of future warming. The change in C and N allocation in adapted sponges suggests a potential adaptation mechanism that allows R. globostellata to survive under thermal stress, but it could alter the availability of essential sources of energy with unknown consequences on the future reef trophic interactions.

Easy interval method, an alternative approach to improve anaerobic threshold speed

This paper presents the specific training method in distance running developed by the Dutch running coach Herman Verheul and successfully applied in the 1970s, its historical background, its afterlife, and its possible applications. The Verheul method can be traced back to the Stampfl interval method used in the 1960s. It involves the use of shorter (200 and 400 meters) and longer (1000 and later 2000 meters) interval training sessions on a daily basis. The method is characterised by a low intensity of partial distances compared to traditional interval training and a relatively long active recovery period equal to the interval distance. For these reasons, the workouts remain predominantly aerobic, without significant lactic acid accumulation, allowing interval training to be used daily and avoiding over-exertion. The method allows runners to achieve high weekly volumes at high running speeds, crucial for establishing anaerobic threshold speed (vLT2) and economical running movement (RE), essential for successful distance running performance. The easy interval method offers an alternative means of improving anaerobic threshold speed. It may provide a new tool to the recently used sustained tempo runs and the anaerobic threshold intervals that are prevalent today.

A Spinal MRI Image Segmentation Method Based on Improved Swin-UNet

ABSTRACT As the number of patients increases, physicians are dealing with more and more cases of degenerative spine pathologies on a daily basis. To reduce the workload of healthcare professionals, we propose a modified Swin-UNet network model. Firstly, the Swin Transformer Blocks are improved using a residual post-normalization and scaling cosine attention mechanism, which makes the training process of the model more stable and improves the accuracy. Secondly, we use the log-space continuous position biasing method instead of the bicubic interpolation position biasing method. This method solves the problem of performance loss caused by the large difference between the resolution of the pretraining image and the resolution of the spine image. Finally, we introduce a segmentation smooth module (SSM) at the decoder stage. The SSM effectively reduces redundancy, and enhances the segmentation edge processing to improve the model’s segmentation accuracy. To validate the proposed method, we conducted experiments on a real dataset provided by hospitals. The average segmentation accuracy is no less than 95%. The experimental results demonstrate the superiority of the proposed method over the original model and other models of the same type in segmenting the spinous processes of the vertebrae and the posterior arch of the spine.

Prediction of Severe Epidemics of Chickpea Ascochyta Blight Using Weather Variables

ABSTRACTChickpea production is threatened by severe epidemics of Ascochyta blight occurring in main chickpea growing lands under appropriate weather conditions worldwide. In this 4‐year research, occurrence of Ascochyta blight was monitored across nine main chickpea growing areas of Kermanshah province, western part of Iran. Each year, commercial chickpea fields were studied on a weekly basis from March to June. Disease data were collected as disease incidence (percentage of infected plants) and severity (percentage of infected tissues) and occurrence of epidemics. Weather data were collected as air temperature, rainfall, and relative humidity (RH) on a daily basis. According to a factor analysis, which explained 83% of data variance, 13 weather predictors were selected to estimate disease epidemics developed across different areas. Before modeling, a principal component analysis determined predictive values for these selected weather variables. Then, eight predictors of rainy days in March and April, mean RH in February, mean minimum temperature in January–March–April, and rainfalls in May and June were involved in model based on their predictive values. Current findings advanced our knowledge on the best weather predictors of severe epidemics of Ascochyta blight in chickpea crops at large scale.

Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSA-Elman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration. In AM-SSA, firstly, the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow, enhancing the global search ability. Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered, expanding the local search ability. Finally, the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal. In addition, it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA. To demonstrate the effectiveness and reliability of AM-SSA, 23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions (CEC2005), are employed as the numerical examples and investigated in comparison with some well-known optimization algorithms. The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces. By utilizing the AdaBoost algorithm, multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output. Additionally, the on-site monitoring data acquired from a deep excavation project in Ningbo, China, were selected as the training and testing sample. Meanwhile, the predictive outcomes are compared with those of other different optimization and machine learning techniques. In the end, the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model, illustrating its power and superiority in terms of computational efficiency, accuracy, stability, and robustness. More critically, by observing data in real time on daily basis, the structural safety associated with metro tunnels could be supervised, which enables decision-makers to take concrete control and protection measures.

Patriarchy and Communalism in Mahesh Dattani’s Final Solutions

Mahesh Dattani’s themes highlight the problems that so many urban people face on a daily basis. These individuals may be living in historical transitions, torn between the alluring modernism and globalisation and the firm grasp of tradition and societal values. Skilled playwright and theatre professional Dattani discovers the problem, develops characters, and employs time, place, and the stage all in service of the subject he addresses. Indian urbanites of today make up the characters on stage. They work with socio-cultural tensions as their issues, and expressionism is his method. This article explains the patriarchy of these individuals in Final Solutions of Mahesh Dattani.

Modeling Self Help Groups’ Impact on Livelihoods in Murang’a East Sub-County: A Logistic Regression Approach

According to the World Bank (2022), approximately 8.9 million people, or 17% of Kenya’s population, live below the poverty line of 1.9 USD on a daily basis, majority of them in the rural areas. This research aimed to analyze the impact of self-help groups on the livelihoods of rural areas of Kenya, with the goal of promoting sustainable livelihoods and reducing poverty. To achieve this, the study employed machine learning specifically the logistic regression algorithm to model the impact of self-help groups on livelihoods in Murang’a East sub-county. The study used primary data obtained through the issuance of structured questionnaires to SHG members, on their wealth status since joining the self-help groups on areas such as ability to save, access to credit services and acquiring assets, both income generating and household. A total of 969 members of self-help groups were issued with the questionnaire. The study’s findings helped identify the key predictors of members’ livelihoods and provided insights into how self-help groups influence them. The results of logistic regression indicated that 91.33% of the members had seen a significant improvement on their wealth status since joining self-help groups and the significant predictor variables were income generating assets, access to basic commodities and access to loans. The model’s accuracy was 88.04%. The ethical considerations in this study included ensuring no coercion or pressure to participate in the study and confidentiality and privacy of the respondents.

Jewish/non-Jewish encounters in corridors and staircases: narrowing down everyday life in liminal spaces

ABSTRACT Contrary to historiographical narratives about Jewish seclusion in European cities at the turn of the twentieth century, we argue that Jews and non-Jews mingled and developed relationships on a daily basis in residential and everyday spaces. We develop the concept of liminal topographies to show how the transient, in-between material structures of staircases and corridors in two such disparate case studies as Stockholm and Vienna facilitated Jewish/non-Jewish relational arenas for the broader masses. Combining digital mapping, visual analysis and text analysis, this approach expands the field of Jewish spatiality by underlining the link between absolute spaces and relational processes.

Mental well-being and difficulties of Polish teachers during remote education in the COVID-19 Pandemic

The SARS-CoV-2 pandemic negatively affected the mental and social well-being of people worldwide. While most research focused on children's difficulties, such as mood changes and social withdrawal, few studies explored the challenges faced by teachers, especially in the context of mental health. This study aimed to understand teachers' views on their well-being during and before COVID-19. A survey of 216 teachers was conducted, of whom 115 worked in mainstream primary schools and 101 in special schools in Poland, during a period of severe pandemic conditions and social isolation measures. The findings showed that the pandemic had significant effects on teachers' mental health in some aspects, but this was not always negative. This highlights the need to prioritize teachers' well-being, not only during crises but also on a daily basis. Ultimately, the quality of children's education depends on the mental state of their teachers.